2025
Journal Articles
1.
Qi, Wenhao; Shen, Shiying; dong, Chaoqun; Zhao, Mengjiao; Zang, Shuaiqi; Zhu, Xiaohong; Li, Jiaqi; Wang, Bin; Shi, Yankai; Dong, Yongze; Shen, Huajuan; Kang, Junling; Lu, Xiaodong; Jiang, Guowei; Du, Jingsong; Shu, Eryi; Zhou, Qingbo; Wang, Jinghua; Cao, Shihua
Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait Journal Article
In: Journal of Medical Internet Research, vol. 27, 2025.
@article{Qi2025,
title = {Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait},
author = {Wenhao Qi and Shiying Shen and Chaoqun dong and Mengjiao Zhao and Shuaiqi Zang and Xiaohong Zhu and Jiaqi Li and Bin Wang and Yankai Shi and Yongze Dong and Huajuan Shen and Junling Kang and Xiaodong Lu and Guowei Jiang and Jingsong Du and Eryi Shu and Qingbo Zhou and Jinghua Wang and Shihua Cao},
url = {https://www.jmir.org/2025/1/e71560/
https://www.jmir.org/2025/1/e71560/PDF},
doi = {10.2196/71560},
year = {2025},
date = {2025-05-20},
journal = {Journal of Medical Internet Research},
volume = {27},
abstract = {Background: With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.
Objective: This study aimed to comprehensively evaluate the current status, hot spots, and future trends of global PD biomarker research, and provide a systematic review of deep learning models for freezing of gait (FOG) digital biomarkers.
Methods: This study used bibliometric analysis based on the Web of Science Core Collection database to conduct a comprehensive analysis of the multidimensional landscape of Parkinson digital biomarkers. After identifying research hot spots, the study also followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for a scoping review of deep learning models for FOG from 5 databases: Web of Science, PubMed, IEEE Xplore, Embase, and Google Scholar.
Results: A total of 750 studies were included in the bibliometric analysis, and 40 studies were included in the scoping review. The analysis revealed a growing number of related publications, with 3700 researchers contributing. Neurology had the highest average annual participation rate (12.46/19, 66%). The United States contributed the most research (192/1171, 16.4%), with 210 participating institutions, which was the highest among all countries. In the study of deep learning models for FOG, the average accuracy of the models was 0.92, sensitivity was 0.88, specificity was 0.90, and area under the curve was 0.91. In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures.
Conclusions: Research on digital biomarkers for PD is currently at a stable stage of development, with widespread global interest from countries, institutions, and researchers. However, challenges remain, including insufficient interdisciplinary and interinstitutional collaboration, as well as a lack of corporate funding for related projects. Current research trends primarily focus on motor-related studies, particularly FOG monitoring. However, deep learning models for FOG still lack external validation and standardized performance reporting. Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.
Objective: This study aimed to comprehensively evaluate the current status, hot spots, and future trends of global PD biomarker research, and provide a systematic review of deep learning models for freezing of gait (FOG) digital biomarkers.
Methods: This study used bibliometric analysis based on the Web of Science Core Collection database to conduct a comprehensive analysis of the multidimensional landscape of Parkinson digital biomarkers. After identifying research hot spots, the study also followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for a scoping review of deep learning models for FOG from 5 databases: Web of Science, PubMed, IEEE Xplore, Embase, and Google Scholar.
Results: A total of 750 studies were included in the bibliometric analysis, and 40 studies were included in the scoping review. The analysis revealed a growing number of related publications, with 3700 researchers contributing. Neurology had the highest average annual participation rate (12.46/19, 66%). The United States contributed the most research (192/1171, 16.4%), with 210 participating institutions, which was the highest among all countries. In the study of deep learning models for FOG, the average accuracy of the models was 0.92, sensitivity was 0.88, specificity was 0.90, and area under the curve was 0.91. In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures.
Conclusions: Research on digital biomarkers for PD is currently at a stable stage of development, with widespread global interest from countries, institutions, and researchers. However, challenges remain, including insufficient interdisciplinary and interinstitutional collaboration, as well as a lack of corporate funding for related projects. Current research trends primarily focus on motor-related studies, particularly FOG monitoring. However, deep learning models for FOG still lack external validation and standardized performance reporting. Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms.
Objective: This study aimed to comprehensively evaluate the current status, hot spots, and future trends of global PD biomarker research, and provide a systematic review of deep learning models for freezing of gait (FOG) digital biomarkers.
Methods: This study used bibliometric analysis based on the Web of Science Core Collection database to conduct a comprehensive analysis of the multidimensional landscape of Parkinson digital biomarkers. After identifying research hot spots, the study also followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for a scoping review of deep learning models for FOG from 5 databases: Web of Science, PubMed, IEEE Xplore, Embase, and Google Scholar.
Results: A total of 750 studies were included in the bibliometric analysis, and 40 studies were included in the scoping review. The analysis revealed a growing number of related publications, with 3700 researchers contributing. Neurology had the highest average annual participation rate (12.46/19, 66%). The United States contributed the most research (192/1171, 16.4%), with 210 participating institutions, which was the highest among all countries. In the study of deep learning models for FOG, the average accuracy of the models was 0.92, sensitivity was 0.88, specificity was 0.90, and area under the curve was 0.91. In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures.
Conclusions: Research on digital biomarkers for PD is currently at a stable stage of development, with widespread global interest from countries, institutions, and researchers. However, challenges remain, including insufficient interdisciplinary and interinstitutional collaboration, as well as a lack of corporate funding for related projects. Current research trends primarily focus on motor-related studies, particularly FOG monitoring. However, deep learning models for FOG still lack external validation and standardized performance reporting. Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms.
2.
Shen, Shiying; Wenhao,; Liu, Xin; Zeng, Jianwen; Li, Sixie; Zhu, Xiaohong; Dong, Chaoqun; Wang, Bin; Shi, Yankai; Yao, Jiani; Wang, Bingsheng; Jing, Louxia; Cao, Shihua; Liang, Guanmian
From virtual to reality: innovative practices of digital twins in tumor therapy Journal Article
In: Journal of Translational Medicine, vol. 23, iss. 348, 2025.
@article{Shen2025,
title = {From virtual to reality: innovative practices of digital twins in tumor therapy},
author = {Shiying Shen and Wenhao and Xin Liu and Jianwen Zeng and Sixie Li and Xiaohong Zhu and Chaoqun Dong and Bin Wang and Yankai Shi and Jiani Yao and Bingsheng Wang and Louxia Jing and Shihua Cao and Guanmian Liang},
url = {https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-025-06371-z},
doi = {/10.1186/s12967-025-06371-z},
year = {2025},
date = {2025-03-19},
urldate = {2025-03-19},
journal = {Journal of Translational Medicine},
volume = {23},
issue = {348},
abstract = {Background As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.
Objective This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.
Methods Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.
Results Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved.
However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment
decision-making, prognosis prediction, and surgical planning.
Conclusion Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.
Objective This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.
Methods Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.
Results Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved.
However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment
decision-making, prognosis prediction, and surgical planning.
Conclusion Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.
Objective This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.
Methods Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.
Results Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved.
However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment
decision-making, prognosis prediction, and surgical planning.
Conclusion Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.
2024
Journal Articles
3.
Li, Bo; Xu, Zeshui; Wang, Xinxin
Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion Journal Article
In: Soft Computing, vol. 28, pp. 9371–9386, 2024.
@article{Li2024,
title = {Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion},
author = {Bo Li and Zeshui Xu and Xinxin Wang},
url = {https://link.springer.com/article/10.1007/s00500-024-09789-7},
doi = {/10.1007/s00500-024-09789-7},
year = {2024},
date = {2024-07-05},
journal = {Soft Computing},
volume = {28},
pages = {9371–9386},
abstract = {Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm intelligence, artificial intelligence, risk management, financial monitoring, etc. With the development of CI, abundant publications have arisen related to many research directions and hotspots. Based on the technical support from bibliometrics and the corresponding approach as well as the content analysis, this study conducts a science mapping analysis and a coherent knowledge picture of the research field in CI. The research contributes to clear future development trends and provides more ideas for scholars in this field. First, this paper focuses on the fundamental characteristics of CI publications, including annual numbers, term co-occurrence, and hot research directions, as the preliminary exploration of this field. Then, according to the widely used core database, i.e., Web of Science (WoS), and the technologies of software, VOS viewer, and CiteSpace, the productive institutions, authors, and journals are explored. Next, the corresponding internal characteristics of the CI research are analyzed, including the citation features of countries/regions, institutions, journals and authors. Furthermore, to analyze the development trend of research hotspots, the keywords of all CI publications are studied: (a) classifying them into three phases in chronological order aimed; (b) implementing the burst detection algorithm to intuitively reflect the scientific research in the field of CI. Finally, this paper provides a relatively throughout perspective for the CI articles and reviews and discloses the future development trend, which will help the scholars interested in this area conduct deep research. We conclude this bibliometric overview with the limitations and recommendations for future research in the field of CI.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm intelligence, artificial intelligence, risk management, financial monitoring, etc. With the development of CI, abundant publications have arisen related to many research directions and hotspots. Based on the technical support from bibliometrics and the corresponding approach as well as the content analysis, this study conducts a science mapping analysis and a coherent knowledge picture of the research field in CI. The research contributes to clear future development trends and provides more ideas for scholars in this field. First, this paper focuses on the fundamental characteristics of CI publications, including annual numbers, term co-occurrence, and hot research directions, as the preliminary exploration of this field. Then, according to the widely used core database, i.e., Web of Science (WoS), and the technologies of software, VOS viewer, and CiteSpace, the productive institutions, authors, and journals are explored. Next, the corresponding internal characteristics of the CI research are analyzed, including the citation features of countries/regions, institutions, journals and authors. Furthermore, to analyze the development trend of research hotspots, the keywords of all CI publications are studied: (a) classifying them into three phases in chronological order aimed; (b) implementing the burst detection algorithm to intuitively reflect the scientific research in the field of CI. Finally, this paper provides a relatively throughout perspective for the CI articles and reviews and discloses the future development trend, which will help the scholars interested in this area conduct deep research. We conclude this bibliometric overview with the limitations and recommendations for future research in the field of CI.
4.
Rikap, Cecilia
In: Review of inteRnational Political economy, 2024.
@article{Rikap2024,
title = {Varieties of corporate innovation systems and their interplay with global and national systems: Amazon, Facebook, Google and Microsoft’s strategies to produce and appropriate artificial intelligence},
author = {Cecilia Rikap},
url = {https://www.tandfonline.com/doi/pdf/10.1080/09692290.2024.2365757},
doi = {https://doi.org/10.1016/j.seps.2024.102087},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
journal = {Review of inteRnational Political economy},
abstract = {The widely accepted globalization of innovation entails two interrelated undertheorized aspects: (1) the capacity of certain firms to orchestrate transnational innovation systems appropriating successful results, which some have explained with the concept of corporate innovation systems (CIS), and (2) the co-existence of such globalization with those CIS and national innovation systems. I address these matters analysing US Big Tech artificial intelligence (AI) CIS showing that they combine multiple mechanisms to co-produce and appropriate AI. I propose ‘frenemy’ to describe Microsoft’s strategy because many Chinese organizations and even direct competitors integrate its CIS. ‘University’ symbolises Google’s strategy, given its focus on fundamental AI, its central place in the AI research field and appropriation mechanisms that are not translating into clear business advantages. ‘Secrecy’ defines Amazon’s strategy, maximizing knowledge inflows while minimizing outflows. Facebook, with the narrowest AI CIS, exhibits an ‘application-centred’ strategy. Ultimately, this paper contributes to understanding the multiple mechanisms used by leading corporations for controlling and shaping frontier transnational knowledge production and appropriation. By doing so, it advances our knowledge of the interplay between different innovation spheres (national, global and corporate) and highlights the dangers of CIS’s encroachment of national and global systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The widely accepted globalization of innovation entails two interrelated undertheorized aspects: (1) the capacity of certain firms to orchestrate transnational innovation systems appropriating successful results, which some have explained with the concept of corporate innovation systems (CIS), and (2) the co-existence of such globalization with those CIS and national innovation systems. I address these matters analysing US Big Tech artificial intelligence (AI) CIS showing that they combine multiple mechanisms to co-produce and appropriate AI. I propose ‘frenemy’ to describe Microsoft’s strategy because many Chinese organizations and even direct competitors integrate its CIS. ‘University’ symbolises Google’s strategy, given its focus on fundamental AI, its central place in the AI research field and appropriation mechanisms that are not translating into clear business advantages. ‘Secrecy’ defines Amazon’s strategy, maximizing knowledge inflows while minimizing outflows. Facebook, with the narrowest AI CIS, exhibits an ‘application-centred’ strategy. Ultimately, this paper contributes to understanding the multiple mechanisms used by leading corporations for controlling and shaping frontier transnational knowledge production and appropriation. By doing so, it advances our knowledge of the interplay between different innovation spheres (national, global and corporate) and highlights the dangers of CIS’s encroachment of national and global systems.
5.
Gourlet, Pauline; Ricci, Donato; Crépel, Maxime
Reclaiming artificial intelligence accounts: A plea for a participatory turn in artificial intelligence inquiries Journal Article
In: Big Data & Society, 2024.
@article{Gourlet2024,
title = {Reclaiming artificial intelligence accounts: A plea for a participatory turn in artificial intelligence inquiries},
author = {Pauline Gourlet and Donato Ricci and Maxime Crépel},
url = {https://journals.sagepub.com/doi/pdf/10.1177/20539517241248093},
doi = {0.1177/20539517241248093},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Big Data & Society},
abstract = {How to participate in artificial intelligence otherwise? Put simply, when it comes to technological developments, participation is either understood as public debates with non-expert voices to anticipate risks and potential harms, or as a way to better design technical systems by involving diverse stakeholders in the design process. We advocate for a third path that considers participation as crucial to problematise what is at stake and to get a grip on the situated developments of artificial intelligence technologies.
This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies.
Taking France as a field of study, we first inspected how media narratives account for the entities and issues of artificial intelligence, as reported by the national press over the last decade. From this inspection, we identified four genres and described their performative effects. We then conducted a participatory inquiry with 25 French artificial intelligence practitioners’ to ground artificial intelligence in situated experiences and trajectories. These experiential accounts enabled a plural problematisation of artificial intelligence, playing with the geometries of artificial intelligence and its constituencies, while diversifying and thickening its problems.
To conclude, we discuss how participatory inquiries, through experiential and plural accounts offer a refreshing weaving of artificial intelligence problems into the fabric of its deployments. Our participatory approach seeks to re-politicise artificial intelligence from practitioners’ situated experiences, by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable, opening avenues to contribute otherwise.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
How to participate in artificial intelligence otherwise? Put simply, when it comes to technological developments, participation is either understood as public debates with non-expert voices to anticipate risks and potential harms, or as a way to better design technical systems by involving diverse stakeholders in the design process. We advocate for a third path that considers participation as crucial to problematise what is at stake and to get a grip on the situated developments of artificial intelligence technologies.
This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies.
Taking France as a field of study, we first inspected how media narratives account for the entities and issues of artificial intelligence, as reported by the national press over the last decade. From this inspection, we identified four genres and described their performative effects. We then conducted a participatory inquiry with 25 French artificial intelligence practitioners’ to ground artificial intelligence in situated experiences and trajectories. These experiential accounts enabled a plural problematisation of artificial intelligence, playing with the geometries of artificial intelligence and its constituencies, while diversifying and thickening its problems.
To conclude, we discuss how participatory inquiries, through experiential and plural accounts offer a refreshing weaving of artificial intelligence problems into the fabric of its deployments. Our participatory approach seeks to re-politicise artificial intelligence from practitioners’ situated experiences, by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable, opening avenues to contribute otherwise.
This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies.
Taking France as a field of study, we first inspected how media narratives account for the entities and issues of artificial intelligence, as reported by the national press over the last decade. From this inspection, we identified four genres and described their performative effects. We then conducted a participatory inquiry with 25 French artificial intelligence practitioners’ to ground artificial intelligence in situated experiences and trajectories. These experiential accounts enabled a plural problematisation of artificial intelligence, playing with the geometries of artificial intelligence and its constituencies, while diversifying and thickening its problems.
To conclude, we discuss how participatory inquiries, through experiential and plural accounts offer a refreshing weaving of artificial intelligence problems into the fabric of its deployments. Our participatory approach seeks to re-politicise artificial intelligence from practitioners’ situated experiences, by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable, opening avenues to contribute otherwise.
Proceedings
6.
Gornet, Mélanie; Delarue, Simon; Boritchev, Maria; Viard, Tiphaine
Mapping AI ethics: a meso-scale analysis of its charters and manifestos Proceedings
2024 ACM Conference on Fairness, Accountability, and Transparency 2024, (FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency).
@proceedings{Gornet2024,
title = {Mapping AI ethics: a meso-scale analysis of its charters and manifestos},
author = {Mélanie Gornet and Simon Delarue and Maria Boritchev and Tiphaine Viard},
url = {https://dl.acm.org/doi/10.1145/3630106.3658545
https://dl.acm.org/doi/pdf/10.1145/3630106.3658545},
doi = {/10.1145/3630106.3658545},
year = {2024},
date = {2024-06-05},
pages = {127-140},
organization = {2024 ACM Conference on Fairness, Accountability, and Transparency},
abstract = {The recent years have seen a surge of initiatives with the goal of defining what “ethical” artificial intelligence would or should entail, resulting in the publication of various charters and manifestos discussing AI ethics; these documents originate from academia, AI industry companies, non-profits, regulatory institutions, and the civil society. The contents of such documents vary wildly, from short, vague position statements to verbatims of democratic debates or impact assessment studies. As such, they are a marker of the social world of artificial intelligence, outlining the tenets of different actors, the consensus and dissensus on important goals, and so on.
Multiple meta-analyses have focused on qualitatively identifying recurring themes in these documents, highlighting the high polysemy of themes such as transparency or trust, among others. The broad term of “AI ethics” and its guiding principles hide multiple disparities, shaped by our collective imaginations, economic and regulatory incentives, and the pre-existing social and structural power asymmetries; through quantitative analyses, we validate and infirm previous qualitative results.
In this paper, we create and present a corpus of charters and manifestos discussing AI ethics through the process of collection and its quantitative analysis using text analysis to shed light on common and distinct vocabularies. Through frequency analysis, hierarchical topic clustering and semantic graph modelling, we show that the charters and manifestos discuss AI ethics along three broad axes: technical documents, regulatory ones, and innovation and business ones. We use our quantitative analysis to back up and nuance previous qualitative results, showing how some themes remain specific while others have fully permeated the space of AI ethics. We document and release our corpus, comprising of 436 documents, charters and manifestos discussing AI ethics. We release the corpus, its datasheet and our analysis, to open the way to further studies and discussions around vocabulary, principles and their evolution, as well as interactions among actors of AI ethics, in order to foster further studies on the topic.},
note = {FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
The recent years have seen a surge of initiatives with the goal of defining what “ethical” artificial intelligence would or should entail, resulting in the publication of various charters and manifestos discussing AI ethics; these documents originate from academia, AI industry companies, non-profits, regulatory institutions, and the civil society. The contents of such documents vary wildly, from short, vague position statements to verbatims of democratic debates or impact assessment studies. As such, they are a marker of the social world of artificial intelligence, outlining the tenets of different actors, the consensus and dissensus on important goals, and so on.
Multiple meta-analyses have focused on qualitatively identifying recurring themes in these documents, highlighting the high polysemy of themes such as transparency or trust, among others. The broad term of “AI ethics” and its guiding principles hide multiple disparities, shaped by our collective imaginations, economic and regulatory incentives, and the pre-existing social and structural power asymmetries; through quantitative analyses, we validate and infirm previous qualitative results.
In this paper, we create and present a corpus of charters and manifestos discussing AI ethics through the process of collection and its quantitative analysis using text analysis to shed light on common and distinct vocabularies. Through frequency analysis, hierarchical topic clustering and semantic graph modelling, we show that the charters and manifestos discuss AI ethics along three broad axes: technical documents, regulatory ones, and innovation and business ones. We use our quantitative analysis to back up and nuance previous qualitative results, showing how some themes remain specific while others have fully permeated the space of AI ethics. We document and release our corpus, comprising of 436 documents, charters and manifestos discussing AI ethics. We release the corpus, its datasheet and our analysis, to open the way to further studies and discussions around vocabulary, principles and their evolution, as well as interactions among actors of AI ethics, in order to foster further studies on the topic.
Multiple meta-analyses have focused on qualitatively identifying recurring themes in these documents, highlighting the high polysemy of themes such as transparency or trust, among others. The broad term of “AI ethics” and its guiding principles hide multiple disparities, shaped by our collective imaginations, economic and regulatory incentives, and the pre-existing social and structural power asymmetries; through quantitative analyses, we validate and infirm previous qualitative results.
In this paper, we create and present a corpus of charters and manifestos discussing AI ethics through the process of collection and its quantitative analysis using text analysis to shed light on common and distinct vocabularies. Through frequency analysis, hierarchical topic clustering and semantic graph modelling, we show that the charters and manifestos discuss AI ethics along three broad axes: technical documents, regulatory ones, and innovation and business ones. We use our quantitative analysis to back up and nuance previous qualitative results, showing how some themes remain specific while others have fully permeated the space of AI ethics. We document and release our corpus, comprising of 436 documents, charters and manifestos discussing AI ethics. We release the corpus, its datasheet and our analysis, to open the way to further studies and discussions around vocabulary, principles and their evolution, as well as interactions among actors of AI ethics, in order to foster further studies on the topic.
2023
Journal Articles
7.
Rizvi, Mohammed
Investigating Al-Powered Tutoring Systems that Adapt to Individual Student Needs, Providing Personalized Guidance and Assessments Journal Article
In: The Eurasia Proceedings of Educational and Social Sciences, vol. 31, pp. 67-73, 2023.
@article{Rizvi2023,
title = {Investigating Al-Powered Tutoring Systems that Adapt to Individual Student Needs, Providing Personalized Guidance and Assessments},
author = {Mohammed Rizvi},
url = {https://www.researchgate.net/profile/Suraj-Patel-48/publication/385701461_Investigating_AI-Powered_Tutoring_Systems/links/6731874f77b63d1220e9bde7/Investigating-AI-Powered-Tutoring-Systems.pdf},
doi = {/10.55549/epess.1381518},
year = {2023},
date = {2023-10-31},
journal = {The Eurasia Proceedings of Educational and Social Sciences},
volume = {31},
pages = {67-73},
abstract = {This comprehensive literature review seeks to assess the potential of Al-powered tutoring systems that are able to adapt and provide personalized guidance tailored to individual student needs. As Artificial Intelligence (AI) technologies continue to progress at a rapid rate, there is ever increasing interest in leveraging these capabilities for educational purposes. By offering customized instruction based on each student's strengths, weaknesses, and learning style preferences, Al-powered tutoring systems may revolutionize how students learn.
The review will examine various studies and research papers exploring the design, implementation techniques as well as effectiveness of such innovative solutions. This includes delving into algorithms like machine learning, natural language processing or data mining which enable these systems to adjust their interactions according to students' requirements. Moreover, it will investigate any positive impacts such personalized teaching has had on academic performance levels in addition to engagement motivation amongst learners. Additionally, this study shall look into existing challenges faced when using Al-powered tutoring systems; from ethical concerns about privacy issues thought too effective teacher -student communication. After taking all findings from available literature into account we can then identify areas where more work is needed, offer suggestions for future improvements or studies within this field. In conclusion, with our synthesis of insights gathered during our investigation we hope improve awareness & understandings around utilizing Al technology for educational purposes so that teachers & students alike can benefit from personalized adaptive educations experiences.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This comprehensive literature review seeks to assess the potential of Al-powered tutoring systems that are able to adapt and provide personalized guidance tailored to individual student needs. As Artificial Intelligence (AI) technologies continue to progress at a rapid rate, there is ever increasing interest in leveraging these capabilities for educational purposes. By offering customized instruction based on each student's strengths, weaknesses, and learning style preferences, Al-powered tutoring systems may revolutionize how students learn.
The review will examine various studies and research papers exploring the design, implementation techniques as well as effectiveness of such innovative solutions. This includes delving into algorithms like machine learning, natural language processing or data mining which enable these systems to adjust their interactions according to students' requirements. Moreover, it will investigate any positive impacts such personalized teaching has had on academic performance levels in addition to engagement motivation amongst learners. Additionally, this study shall look into existing challenges faced when using Al-powered tutoring systems; from ethical concerns about privacy issues thought too effective teacher -student communication. After taking all findings from available literature into account we can then identify areas where more work is needed, offer suggestions for future improvements or studies within this field. In conclusion, with our synthesis of insights gathered during our investigation we hope improve awareness & understandings around utilizing Al technology for educational purposes so that teachers & students alike can benefit from personalized adaptive educations experiences.
The review will examine various studies and research papers exploring the design, implementation techniques as well as effectiveness of such innovative solutions. This includes delving into algorithms like machine learning, natural language processing or data mining which enable these systems to adjust their interactions according to students' requirements. Moreover, it will investigate any positive impacts such personalized teaching has had on academic performance levels in addition to engagement motivation amongst learners. Additionally, this study shall look into existing challenges faced when using Al-powered tutoring systems; from ethical concerns about privacy issues thought too effective teacher -student communication. After taking all findings from available literature into account we can then identify areas where more work is needed, offer suggestions for future improvements or studies within this field. In conclusion, with our synthesis of insights gathered during our investigation we hope improve awareness & understandings around utilizing Al technology for educational purposes so that teachers & students alike can benefit from personalized adaptive educations experiences.
8.
Lim, Tristan
Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways Journal Article
In: 2023.
@article{Lim2023,
title = {Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways},
author = {Tristan Lim},
url = {https://europepmc.org/article/ppr/ppr651155
https://www.researchgate.net/profile/Tristan_Lim5/publication/370228369_Environmental_Social_and_Governance_ESG_and_Artificial_Intelligence_in_Finance_State-of-the-Art_and_Research_Takeaways/links/64474633017bc07902d798dc/Environmental-Social-and-Governance-ESG-and-Artificial-Intelligence-in-Finance-State-of-the-Art-and-Research-Takeaways.pdf},
doi = {10.21203/rs.3.rs-2849051/v1},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
abstract = {The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation centers around three research questions: key research themes for ESG and AI in finance, research intensity and interest evolution, and the use and progression of AI techniques within these themes. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation centers around three research questions: key research themes for ESG and AI in finance, research intensity and interest evolution, and the use and progression of AI techniques within these themes. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.
9.
Lim, Tristan; Gottipati, Swapna; Cheong, Michelle
Articial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment Journal Article Forthcoming
In: Forthcoming.
@article{Lim2023c,
title = {Articial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment},
author = {Tristan Lim and Swapna Gottipati and Michelle Cheong},
url = {https://www.researchsquare.com/article/rs-2696273/v1},
doi = {/10.21203/rs.3.rs-2696273/v1},
year = {2023},
date = {2023-03-16},
abstract = {In a societal institution as fundamental as education, when teaching practitioners or researchers apply artificial intelligence in academic processes such as assessments, it is important to study the divide between what may be ethically permissible and not permissible. This study applied a systematic literature mapping methodology to scour extant research, so as to holistically
structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped ten key ethical principles to five research archetypical domains, and reviewed the contribution and intensity of these ethical principles in each thematic domain. The study extended this review, by mapping out ethics programs and activities that can be applied in practice, alongside their relevant underpinning theories. This study provides a comprehensive treatment of this subject matter to date. We hope the findings of this research can provide researchers and practitioners the insights into the application
methods of AI in assessments, and in particular, in terms of their intertwined ethical challenges and how these challenges may be addressed, for follow up studies.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
In a societal institution as fundamental as education, when teaching practitioners or researchers apply artificial intelligence in academic processes such as assessments, it is important to study the divide between what may be ethically permissible and not permissible. This study applied a systematic literature mapping methodology to scour extant research, so as to holistically
structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped ten key ethical principles to five research archetypical domains, and reviewed the contribution and intensity of these ethical principles in each thematic domain. The study extended this review, by mapping out ethics programs and activities that can be applied in practice, alongside their relevant underpinning theories. This study provides a comprehensive treatment of this subject matter to date. We hope the findings of this research can provide researchers and practitioners the insights into the application
methods of AI in assessments, and in particular, in terms of their intertwined ethical challenges and how these challenges may be addressed, for follow up studies.
structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped ten key ethical principles to five research archetypical domains, and reviewed the contribution and intensity of these ethical principles in each thematic domain. The study extended this review, by mapping out ethics programs and activities that can be applied in practice, alongside their relevant underpinning theories. This study provides a comprehensive treatment of this subject matter to date. We hope the findings of this research can provide researchers and practitioners the insights into the application
methods of AI in assessments, and in particular, in terms of their intertwined ethical challenges and how these challenges may be addressed, for follow up studies.
Books
10.
Keengwe, Jared
Creative AI Tools and Ethical Implications in Teaching and Learning Book
IGI Global, 2023, ISBN: 9798369302057.
@book{Keengwe2023,
title = {Creative AI Tools and Ethical Implications in Teaching and Learning},
author = {Jared Keengwe},
doi = {10.4018/979-8-3693-0205-7},
isbn = {9798369302057},
year = {2023},
date = {2023-09-01},
urldate = {2023-09-01},
pages = {291},
publisher = {IGI Global},
school = {University of North Dakota},
abstract = {As generative Artificial Intelligence (AI) tools become increasingly prevalent, it is crucial for educators to develop a nuanced understanding of the ethical implications associated with their use. Educators today face the critical task of navigating the pedagogical applications of AI and maximizing its potential to foster student learning. Creative AI Tools and Ethical Implications in Teaching and Learning is an insightful exploration of the ethical considerations surrounding the integration of AI in education. Educators may fear how it will rapidly transforming the educational landscape, but this book serves to support them and delves into the opportunities and challenges that arise when leveraging AI to enhance teaching and learning experiences.
This book sheds light on the various dimensions of AI in education, including face-to-face, blended, and online learning environments. It also offers practical guidance on designing AI-based classroom activities and harnessing open AI technologies to create engaging learning experiences. This book also delves into the pressing issues of bias, misinformation, and access, urging educators to critically evaluate and address these concerns. It also provides ethical guidelines for the responsible integration of AI in education, ensuring that students' rights and well-being are safeguarded.
Creative AI Tools and Ethical Implications in Teaching and Learning goes beyond theory to offer practical strategies for integrating AI creatively into the classroom. From learning analytics and educational data mining to AI game activities and generative AI tools like ChatGPT, this book equips educators with the knowledge and resources to adapt AI technologies to support teaching and learning effectively.
Moreover, the book explores the vital connection between AI and student assessment, highlighting how AI can enhance the evaluation process while maintaining fairness and objectivity. It concludes with an insightful glimpse into the future of AI in education, envisioning the transformative possibilities that lie ahead. This comprehensive guide provides educators, researchers, and policymakers with the tools they need to navigate the complexities of AI in education.},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
As generative Artificial Intelligence (AI) tools become increasingly prevalent, it is crucial for educators to develop a nuanced understanding of the ethical implications associated with their use. Educators today face the critical task of navigating the pedagogical applications of AI and maximizing its potential to foster student learning. Creative AI Tools and Ethical Implications in Teaching and Learning is an insightful exploration of the ethical considerations surrounding the integration of AI in education. Educators may fear how it will rapidly transforming the educational landscape, but this book serves to support them and delves into the opportunities and challenges that arise when leveraging AI to enhance teaching and learning experiences.
This book sheds light on the various dimensions of AI in education, including face-to-face, blended, and online learning environments. It also offers practical guidance on designing AI-based classroom activities and harnessing open AI technologies to create engaging learning experiences. This book also delves into the pressing issues of bias, misinformation, and access, urging educators to critically evaluate and address these concerns. It also provides ethical guidelines for the responsible integration of AI in education, ensuring that students' rights and well-being are safeguarded.
Creative AI Tools and Ethical Implications in Teaching and Learning goes beyond theory to offer practical strategies for integrating AI creatively into the classroom. From learning analytics and educational data mining to AI game activities and generative AI tools like ChatGPT, this book equips educators with the knowledge and resources to adapt AI technologies to support teaching and learning effectively.
Moreover, the book explores the vital connection between AI and student assessment, highlighting how AI can enhance the evaluation process while maintaining fairness and objectivity. It concludes with an insightful glimpse into the future of AI in education, envisioning the transformative possibilities that lie ahead. This comprehensive guide provides educators, researchers, and policymakers with the tools they need to navigate the complexities of AI in education.
This book sheds light on the various dimensions of AI in education, including face-to-face, blended, and online learning environments. It also offers practical guidance on designing AI-based classroom activities and harnessing open AI technologies to create engaging learning experiences. This book also delves into the pressing issues of bias, misinformation, and access, urging educators to critically evaluate and address these concerns. It also provides ethical guidelines for the responsible integration of AI in education, ensuring that students' rights and well-being are safeguarded.
Creative AI Tools and Ethical Implications in Teaching and Learning goes beyond theory to offer practical strategies for integrating AI creatively into the classroom. From learning analytics and educational data mining to AI game activities and generative AI tools like ChatGPT, this book equips educators with the knowledge and resources to adapt AI technologies to support teaching and learning effectively.
Moreover, the book explores the vital connection between AI and student assessment, highlighting how AI can enhance the evaluation process while maintaining fairness and objectivity. It concludes with an insightful glimpse into the future of AI in education, envisioning the transformative possibilities that lie ahead. This comprehensive guide provides educators, researchers, and policymakers with the tools they need to navigate the complexities of AI in education.
Conferences
11.
Abdo, Alexandre Hannud; Benbouzid, Bilel; Turnheim, Bruno; Raimbault, Benjamin; Barbier, Marc
SASHIMI and new frontiers in the study of socio-semantic networks with mixed-methods on the Cortext Platform Conference
Sunbelt 2023 Portland, OR, United States, 2023, (INSNA).
@conference{Abdo2023,
title = {SASHIMI and new frontiers in the study of socio-semantic networks with mixed-methods on the Cortext Platform},
author = { Alexandre Hannud Abdo and Bilel Benbouzid and Bruno Turnheim and Benjamin Raimbault and Marc Barbier},
url = {https://hal.science/hal-04488978/
https://solstag.gitlab.io/presentations/sunbelt2023/
},
year = {2023},
date = {2023-06-28},
address = {Portland, OR, United States},
organization = {Sunbelt 2023 },
abstract = {Since 2008, the Cortext Platform contributes expertise, infrastructure and computing power for the analysis of "socio-semantic networks", benefiting a global community engaged primarily in original research in the social sciences and humanities, but also assisting literature reviews in a host of others fields, as well as policy and business applications. In 2022, at least 60 peer-reviewed academic publications made direct use of our services, adding to a total of over 300. This presentation will focus on SASHIMI (Hannud Abdo, 2021), a network based, mixed-methods approach recently developed in addition to our earlier Network Mapping methods (Cointet 2012, Cointet 2017), available as both a suite of no-code methods in the free-to-use Cortext Manager cloud service, and a free-and-open-source software library. We will present SASHIMI through some examples of socio-semantic analyses: (a) from the field of Transition Studies, an inquiry into the variety of disciplinary manifestations throughout the social sciences of the "research problem of destabilisation of socio-technical systems", that seeks to inform current destabilisation/discontinuation/phase-out studies with a wider understanding of the problem. (b) from the field of Science and Technology Studies, an analysis of policy documents pertaining to the regulation of artificial intelligence, identifying the interplay between major actors associated with different themes, sectors and perspectives (solutionism, contestation, regulation) on the issue; (c) still in STS, an analysis of social media interactions concerning environmental controversies, focusing on the debate around pesticides in France. SASHIMI is based on domain-topic models, an application of network clustering that synthesizes document clustering (or clustering of any kind of hypernode) and topic modeling. It is also based on a suite of human interfaces — block maps, network maps, and hyperlinked tables — that afford interactive exploration and visualization of the different types of clusters, and their relationships, at discrete levels of granularity ranging from the entire corpus to the individual document, from the entire vocabulary to the individual word. The clustering aspect is based on modern community detection methods, namely the Nested Stochastic Block Model (Peixoto, 2015), while introducing a twist to allow further clustering of dimensions attributed to hypernodes (documents), such as people, time, venue or other categorical metadata, that did not participate in the initial clustering — excluded, for example, in order to produce "semantic" document clusters based exclusively on textual contents. To this particular procedure we give the name "chaining". In the context of the three aforementioned examples, we'll explain a set of concepts and practices, emerging from our usage, to productively co-construct meaning between the representations afforded by the models and interfaces, and the goals, inputs and choices of a researcher with field and experiential knowledge. Particularly, how to interpret the clusters and the specificity and commonality scores of inter-cluster relationships employed in the maps, how to build sequences of corpus delimitation and dimension chaining operations and interpret them, and finally how to construct coherent domain groups we call "constellations", and identify attribute flows in their cores and frontiers. },
note = {INSNA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Since 2008, the Cortext Platform contributes expertise, infrastructure and computing power for the analysis of "socio-semantic networks", benefiting a global community engaged primarily in original research in the social sciences and humanities, but also assisting literature reviews in a host of others fields, as well as policy and business applications. In 2022, at least 60 peer-reviewed academic publications made direct use of our services, adding to a total of over 300. This presentation will focus on SASHIMI (Hannud Abdo, 2021), a network based, mixed-methods approach recently developed in addition to our earlier Network Mapping methods (Cointet 2012, Cointet 2017), available as both a suite of no-code methods in the free-to-use Cortext Manager cloud service, and a free-and-open-source software library. We will present SASHIMI through some examples of socio-semantic analyses: (a) from the field of Transition Studies, an inquiry into the variety of disciplinary manifestations throughout the social sciences of the "research problem of destabilisation of socio-technical systems", that seeks to inform current destabilisation/discontinuation/phase-out studies with a wider understanding of the problem. (b) from the field of Science and Technology Studies, an analysis of policy documents pertaining to the regulation of artificial intelligence, identifying the interplay between major actors associated with different themes, sectors and perspectives (solutionism, contestation, regulation) on the issue; (c) still in STS, an analysis of social media interactions concerning environmental controversies, focusing on the debate around pesticides in France. SASHIMI is based on domain-topic models, an application of network clustering that synthesizes document clustering (or clustering of any kind of hypernode) and topic modeling. It is also based on a suite of human interfaces — block maps, network maps, and hyperlinked tables — that afford interactive exploration and visualization of the different types of clusters, and their relationships, at discrete levels of granularity ranging from the entire corpus to the individual document, from the entire vocabulary to the individual word. The clustering aspect is based on modern community detection methods, namely the Nested Stochastic Block Model (Peixoto, 2015), while introducing a twist to allow further clustering of dimensions attributed to hypernodes (documents), such as people, time, venue or other categorical metadata, that did not participate in the initial clustering — excluded, for example, in order to produce "semantic" document clusters based exclusively on textual contents. To this particular procedure we give the name "chaining". In the context of the three aforementioned examples, we'll explain a set of concepts and practices, emerging from our usage, to productively co-construct meaning between the representations afforded by the models and interfaces, and the goals, inputs and choices of a researcher with field and experiential knowledge. Particularly, how to interpret the clusters and the specificity and commonality scores of inter-cluster relationships employed in the maps, how to build sequences of corpus delimitation and dimension chaining operations and interpret them, and finally how to construct coherent domain groups we call "constellations", and identify attribute flows in their cores and frontiers.
Book Chapters
12.
Lim, Tristan; Gottipati, Swapna; Cheong, Michelle L. F.
Ethical Considerations for Artificial Intelligence in Educational Assessments Book Chapter
In: 2023.
@inbook{Lim2023d,
title = {Ethical Considerations for Artificial Intelligence in Educational Assessments},
author = {Tristan Lim and Swapna Gottipati and Michelle L. F. Cheong},
url = {https://www.researchgate.net/publication/376951624_Ethical_Considerations_for_Artificial_Intelligence_in_Educational_Assessments},
doi = {10.4018/979-8-3693-0205-7.ch003},
year = {2023},
date = {2023-12-31},
abstract = {In the vital context of education, the application of artificial intelligence (AI) to assessments necessitates a nuanced examination of the boundaries between ethically permissible and impermissible practices. In this chapter, the authors applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to different assessment phases in a triadic ontological framework. The chapter looks to provide researchers and practitioners the insights into the ethical challenges that exist across an end-to-end assessment pipeline.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
In the vital context of education, the application of artificial intelligence (AI) to assessments necessitates a nuanced examination of the boundaries between ethically permissible and impermissible practices. In this chapter, the authors applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to different assessment phases in a triadic ontological framework. The chapter looks to provide researchers and practitioners the insights into the ethical challenges that exist across an end-to-end assessment pipeline.
2022
Journal Articles
13.
Bai, Yang; Li, Hongxiu
Mapping the evolution of e-commerce research through co-word analysis: 2001–2020 Journal Article
In: Electronic Commerce Research and Applications, vol. 55, pp. 101190, 2022, ISSN: 1567-4223.
@article{Bai2022,
title = {Mapping the evolution of e-commerce research through co-word analysis: 2001–2020},
author = {Yang Bai and Hongxiu Li},
url = {https://www.sciencedirect.com/science/article/pii/S1567422322000746},
doi = {/10.1016/j.elerap.2022.101190},
issn = {1567-4223},
year = {2022},
date = {2022-08-17},
urldate = {2022-08-17},
journal = {Electronic Commerce Research and Applications},
volume = {55},
pages = {101190},
abstract = {This current study examines how research themes and trends have developed in the e-commerce field, aiming to provide a comprehensive understanding of e-commerce research based on prior literature. To answer this question, the current study applied a bibliometric method, word co-occurrence (co-word) analysis, to identify core research themes and trends in the e-commerce field, based on 17,416 author-provided keywords collected from 3,284 academic articles published between 2001 and 2020 in seven major journals in e-commerce field. Specifically, the data were analyzed based on two periods: 2001–2010 and 2011–2020. We found that the research themes in the e-commerce field have evolved alongside technology’s development and diffusion. Some research themes, such as recommended system and eGovernment, have persisted as important research themes and studied continuously over the 20 years studied. Some research themes studied in 2001–2010, such as B2C and XML, disappeared in 2011–2020, whereas some new research topics, such as cloud computing, mobile technologies, artificial intelligence, and machine learning, emerged in recent years. This study provides a good understanding of e-commerce research’s evolution, as well as useful suggestions to direct future research by scholars in the e-commerce field.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This current study examines how research themes and trends have developed in the e-commerce field, aiming to provide a comprehensive understanding of e-commerce research based on prior literature. To answer this question, the current study applied a bibliometric method, word co-occurrence (co-word) analysis, to identify core research themes and trends in the e-commerce field, based on 17,416 author-provided keywords collected from 3,284 academic articles published between 2001 and 2020 in seven major journals in e-commerce field. Specifically, the data were analyzed based on two periods: 2001–2010 and 2011–2020. We found that the research themes in the e-commerce field have evolved alongside technology’s development and diffusion. Some research themes, such as recommended system and eGovernment, have persisted as important research themes and studied continuously over the 20 years studied. Some research themes studied in 2001–2010, such as B2C and XML, disappeared in 2011–2020, whereas some new research topics, such as cloud computing, mobile technologies, artificial intelligence, and machine learning, emerged in recent years. This study provides a good understanding of e-commerce research’s evolution, as well as useful suggestions to direct future research by scholars in the e-commerce field.
14.
Franco, Sebastián Fernández; Graña, Juan M; Flacher, David; Rikap, Cecilia
Producing and using artificial intelligence: What can Europe learn from Siemens’s experience? Journal Article
In: Competition & Change, vol. 0, pp. 1–30, 2022.
@article{Franco2022,
title = {Producing and using artificial intelligence: What can Europe learn from Siemens’s experience?},
author = {Sebastián Fernández Franco and Juan M Graña and David Flacher and Cecilia Rikap},
url = {https://www.researchgate.net/profile/Juan-Grana-2/publication/360759657_Producing_and_using_artificial_intelligence_What_can_Europe_learn_from_Siemens's_experience/links/62a739f955273755ebe9963b/Producing-and-using-artificial-intelligence-What-can-Europe-learn-from-Siemenss-experience.pdf},
doi = {10.1177/10245294221097066},
year = {2022},
date = {2022-06-10},
urldate = {2022-06-10},
journal = {Competition & Change},
volume = {0},
pages = {1–30},
abstract = {This paper examines the innovation strategy of Siemens, a key player in Europe’s digital economy, by performing network and lexical analyses using data derived from Siemens’s patents and scientific publications since 1998. We observe that the company’s innovation efforts evolved from a broader attempt to develop internal information and communication technology (ICT) capabilities – alongside its historical industrial priorities – to a strategy focused on developing artificial intelligence (AI) for sector-specific and niche applications (such as life and medical sciences). As a result, it became dependent on tech giants’ clouds for accessing more general AI services and digital infrastructure. We build on the intellectual monopoly literature focusing on the effects of tech giants on other leading corporations, to analyse Siemens’s experience. By abandoning the development of general ICT and given the emergence of tech giants as digital economy intellectual monopolies, we show that Siemens is risking its technological autonomy towards these big tech companies. Our results provide clues to understand the challenges faced by Europe and its firms in relation to US and Chinese tech giants.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper examines the innovation strategy of Siemens, a key player in Europe’s digital economy, by performing network and lexical analyses using data derived from Siemens’s patents and scientific publications since 1998. We observe that the company’s innovation efforts evolved from a broader attempt to develop internal information and communication technology (ICT) capabilities – alongside its historical industrial priorities – to a strategy focused on developing artificial intelligence (AI) for sector-specific and niche applications (such as life and medical sciences). As a result, it became dependent on tech giants’ clouds for accessing more general AI services and digital infrastructure. We build on the intellectual monopoly literature focusing on the effects of tech giants on other leading corporations, to analyse Siemens’s experience. By abandoning the development of general ICT and given the emergence of tech giants as digital economy intellectual monopolies, we show that Siemens is risking its technological autonomy towards these big tech companies. Our results provide clues to understand the challenges faced by Europe and its firms in relation to US and Chinese tech giants.
15.
Rikap, Cecilia
Becoming an intellectual monopoly by relying on the national innovation system: the State Grid Corporation of China's experience Journal Article
In: Research Policy, vol. 51, no. 4, pp. 104472, 2022, ISSN: 0048-7333.
@article{Rikap2022,
title = {Becoming an intellectual monopoly by relying on the national innovation system: the State Grid Corporation of China's experience},
author = {Cecilia Rikap},
url = {https://www.sciencedirect.com/science/article/pii/S004873332100264X},
doi = {10.1016/j.respol.2021.104472},
issn = {0048-7333},
year = {2022},
date = {2022-05-01},
urldate = {2022-05-01},
journal = {Research Policy},
volume = {51},
number = {4},
pages = {104472},
abstract = {This paper examines the origins of global leaders under intellectual monopoly capitalism. State Grid Corporation of China (SGCC), the leading firm in artificial intelligence applications for the energy sector, became an intellectual monopoly relying heavily on China's national innovation system –particularly public research organizations and public funding, and innovation and energy policies. SGCC is unique because it did not rely on technology transfer from global leaders, unlike other national champions from developing or emerging countries. We provide evidence that contributes to thinking that SGCC first became a national intellectual monopoly and only afterwards expanded that monopoly globally. We empirically study SGCC's innovation networks. We proxy them using big data techniques to analyze the content, co-authors and co-owners of its publications and patents. Results also suggest that SGCC is capturing intellectual rents from its increasingly transnational and technologically diverse innovation networks by leveraging its national innovation system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper examines the origins of global leaders under intellectual monopoly capitalism. State Grid Corporation of China (SGCC), the leading firm in artificial intelligence applications for the energy sector, became an intellectual monopoly relying heavily on China's national innovation system –particularly public research organizations and public funding, and innovation and energy policies. SGCC is unique because it did not rely on technology transfer from global leaders, unlike other national champions from developing or emerging countries. We provide evidence that contributes to thinking that SGCC first became a national intellectual monopoly and only afterwards expanded that monopoly globally. We empirically study SGCC's innovation networks. We proxy them using big data techniques to analyze the content, co-authors and co-owners of its publications and patents. Results also suggest that SGCC is capturing intellectual rents from its increasingly transnational and technologically diverse innovation networks by leveraging its national innovation system.
16.
Lundvall, Bengt-Åke; Rikap, Cecilia
China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems Journal Article
In: Research Policy, vol. 51, no. 1, pp. 104395, 2022, ISSN: 0048-7333.
@article{Lundvall2022,
title = {China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems},
author = {Bengt-Åke Lundvall and Cecilia Rikap},
url = {https://www.sciencedirect.com/science/article/pii/S0048733321001918},
doi = {https://doi.org/10.1016/j.respol.2021.104395},
issn = {0048-7333},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Research Policy},
volume = {51},
number = {1},
pages = {104395},
abstract = {Inspired by Christopher Freeman's work on how radical technical change opens up for shifts in world leadership and on the role of innovation systems in this process, this paper explores China's emergence as a lead country in artificial intelligence as reflecting a co-evolution of Corporate and National Innovation Systems. Taking Freeman's (1987) work on Japan as our lead, we focus on the domestic interaction within and on the openness of China's national innovation system. To follow up on his prediction of the increasing importance of big companies as network leaders, we introduce the concept “corporate innovation system” with special attention to two Chinese tech giants: Alibaba and Tencent.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Inspired by Christopher Freeman's work on how radical technical change opens up for shifts in world leadership and on the role of innovation systems in this process, this paper explores China's emergence as a lead country in artificial intelligence as reflecting a co-evolution of Corporate and National Innovation Systems. Taking Freeman's (1987) work on Japan as our lead, we focus on the domestic interaction within and on the openness of China's national innovation system. To follow up on his prediction of the increasing importance of big companies as network leaders, we introduce the concept “corporate innovation system” with special attention to two Chinese tech giants: Alibaba and Tencent.
2021
Journal Articles
17.
Kabudi, Tumaini; Pappas, Ilias; Olsen, Dag Håkon
AI-enabled adaptive learning systems: A systematic mapping of the literature Journal Article
In: Computers and Education: Artificial Intelligence, vol. 2, pp. 100017, 2021, ISSN: 2666-920X.
@article{Kabudi2021,
title = {AI-enabled adaptive learning systems: A systematic mapping of the literature},
author = {Tumaini Kabudi and Ilias Pappas and Dag Håkon Olsen},
url = {https://www.sciencedirect.com/science/article/pii/S2666920X21000114},
doi = {https://doi.org/10.1016/j.caeai.2021.100017},
issn = {2666-920X},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
journal = {Computers and Education: Artificial Intelligence},
volume = {2},
pages = {100017},
abstract = {Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.
Conferences
18.
Benbouzid, Bilel; Villard, Lionel; Larédo, Philippe; Adie, Euan
Tentative Governance of Artificial Intelligence Regulation. Representing governance as a virtual network of documents. Conference
Eu-SPRI Annual Conference Oslo, 2021.
@conference{Benbouzid2021,
title = {Tentative Governance of Artificial Intelligence Regulation. Representing governance as a virtual network of documents.},
author = {Bilel Benbouzid and Lionel Villard and Philippe Larédo and Euan Adie},
url = {https://www.euspri2021.no/wp-content/uploads/2021/06/Session-8.3.pdf},
year = {2021},
date = {2021-06-09},
address = {Oslo},
organization = {Eu-SPRI Annual Conference},
abstract = {The paper wishes to test our ability to use quantitative methods to track emerging arrangements in the governance of artificial intelligence regulation. We position the paper in the conceptual approach proposed by Kuhlmann Stegmaier and Konrad (2018).},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The paper wishes to test our ability to use quantitative methods to track emerging arrangements in the governance of artificial intelligence regulation. We position the paper in the conceptual approach proposed by Kuhlmann Stegmaier and Konrad (2018).
Proceedings Articles
19.
Crépel, Maxime; Do, Salomé; Cointet, Jean-Philippe; Cardon, Dominique; Bouachera, Yannis
Mapping AI Issues in Media Through NLP Methods Proceedings Article
In: Proceedings of the Conference on Computational Humanities Research 2021, pp. 77-91, CEUR Workshop Proceedings, Amsterdam, the Netherlands, 2021.
@inproceedings{Crépel2021,
title = {Mapping AI Issues in Media Through NLP Methods},
author = {Maxime Crépel and Salomé Do and Jean-Philippe Cointet and Dominique Cardon and Yannis Bouachera},
url = {http://ceur-ws.org/Vol-2989/long_paper22.pdf},
year = {2021},
date = {2021-11-19},
urldate = {2021-11-19},
booktitle = {Proceedings of the Conference on Computational Humanities Research 2021},
pages = {77-91},
publisher = {CEUR Workshop Proceedings},
address = {Amsterdam, the Netherlands},
series = {Computational Humanities Research 2021},
abstract = {Using a variety of NLP methods on a corpus of press articles, we show that there are two dominant regimes of criticism of artificial intelligence that coexist within the media sphere. Combining text classification algorithms to detect critical articles and a topological analysis of the terms extracted from the corpus, we reveal two semantic spaces, involving different technological and human entities, but also distinct temporality and issues. On the one hand, the algorithms that shape our daily computing environments are associated with a critical discourse on bias, discrimination, surveillance, censorship and amplification phenomena in the spread of inappropriate content. On the other hand, robots and AI, which refer to autonomous and embodied technical entities, are associated with a prophetic discourse alerting us to our ability to control these agents that simulate or exceed our physical and cognitive capacities and threaten our physical security or our economic mode.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Using a variety of NLP methods on a corpus of press articles, we show that there are two dominant regimes of criticism of artificial intelligence that coexist within the media sphere. Combining text classification algorithms to detect critical articles and a topological analysis of the terms extracted from the corpus, we reveal two semantic spaces, involving different technological and human entities, but also distinct temporality and issues. On the one hand, the algorithms that shape our daily computing environments are associated with a critical discourse on bias, discrimination, surveillance, censorship and amplification phenomena in the spread of inappropriate content. On the other hand, robots and AI, which refer to autonomous and embodied technical entities, are associated with a prophetic discourse alerting us to our ability to control these agents that simulate or exceed our physical and cognitive capacities and threaten our physical security or our economic mode.
20.
Penteado, Bruno Elias; Fornazin, Marcelo; Castro, Leonardo
The Evolution of Artificial Intelligence in Medical Informatics: A Bibliometric Analysis Proceedings Article
In: EPIA 2021: Progress in Artificial Intelligence, pp. 121-133, 2021.
@inproceedings{Penteado2021c,
title = {The Evolution of Artificial Intelligence in Medical Informatics: A Bibliometric Analysis},
author = {Bruno Elias Penteado and Marcelo Fornazin and Leonardo Castro},
url = {https://link.springer.com/chapter/10.1007/978-3-030-86230-5_10},
doi = {10.1007/978-3-030-86230-5_10},
year = {2021},
date = {2021-09-03},
urldate = {2021-09-03},
booktitle = {EPIA 2021: Progress in Artificial Intelligence},
pages = {121-133},
series = {EPIA Conference on Artificial Intelligence},
abstract = {Artificial intelligence (AI) and medical informatics research fields have considerable overlap, with technologies supporting different health issues in different contexts. In this work, we aimed to map out and understand the contributions of AI in medical informatics over time. To that, we applied bibliometric analysis with scientific literature since the 1970s. The production of papers exponentially increased over time, and we found periods with similar characteristics of the content. We also identified different clusters of technologies and applications varying according to the periods and related keywords. We hypothesized some future directions for the use of AI in medical informatics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Artificial intelligence (AI) and medical informatics research fields have considerable overlap, with technologies supporting different health issues in different contexts. In this work, we aimed to map out and understand the contributions of AI in medical informatics over time. To that, we applied bibliometric analysis with scientific literature since the 1970s. The production of papers exponentially increased over time, and we found periods with similar characteristics of the content. We also identified different clusters of technologies and applications varying according to the periods and related keywords. We hypothesized some future directions for the use of AI in medical informatics.
2020
Journal Articles
21.
Gauld, Christophe; Franchi, Jean-Arthur M.
Analyse en réseau par fouille de données textuelles systématique du concept de psychiatrie personnalisée et de précision Journal Article
In: L'Encéphale, 2020, ISSN: 0013-7006.
@article{Gaulda2020,
title = {Analyse en réseau par fouille de données textuelles systématique du concept de psychiatrie personnalisée et de précision},
author = {Christophe Gauld and Jean-Arthur M. Franchi},
url = {http://www.sciencedirect.com/science/article/pii/S0013700620302360},
doi = { https://doi.org/10.1016/j.encep.2020.08.008},
issn = {0013-7006},
year = {2020},
date = {2020-11-12},
urldate = {2020-11-12},
journal = {L'Encéphale},
abstract = {Objectifs. – La médecine personnalisée et de précision nécessite une clarification des concepts qui y sont rattachés. À notre connaissance, il n’existe pas d’exploration systématique de la littérature portant sur les dimensions et les concepts de la psychiatrie personnalisée et de précision et sur leurs usages dans les domaines neuroscientifiques et génétiques. Cet article propose donc d’explorer les dimensions et les concepts de la psychiatrie personnalisée et de précision.
Méthodes. – Une analyse en réseau par fouille de données textuelles systématique issue d’une revue exhaustive de la littérature internationale autour des termes de “precision psychiatry” et de “personalized psychiatry” a été réalisée. Cette fouille de données textuelles a été représentée sous forme d’un réseau permettant d’analyser les dimensions et les concepts de la psychiatrie personnalisée et de précision. Résultats. – La psychiatrie personnalisée et de précision renvoie à six dimensions retrouvées au sein de l’analyse du réseau textuel. Ces six dimensions correspondent aux domaines scientifiques qui étu- dient la psychiatrie personnalisée et de précision, à savoir : la génétique, la pharmacogénétique, les approches computationnelles, le raffinement des essais thérapeutiques, les biomarqueurs et la stadifica- tion. L’analyse des termes renvoie à un ensemble de concepts hétérogènes.
Conclusions. – L’hétérogénéité retrouvée dans la littérature sur la psychiatrie personnalisée et de précision peut témoigner d’un manque d’un cadre théorique pluraliste et intégratif. Ce cadre de travail pourrait être basé sur un formalisme naturalisant mais non réducteur, conscient des enjeux sociétaux des sciences et de leur implémentation dans les dispositifs de recherche et cliniques de la psychiatrie.
Objectives
The current challenges of psychiatric nosology and semiology are part of an interdisciplinary and integrative framework. The paradigm of the personalized and precision psychiatry proposes to study this discipline according to new approaches and methodologies. Personalized and precision psychiatry therefore requires clarification of its concepts. To our knowledge, there is no systematic exploration of the literature on the application of the concepts of personalized and precision medicine in the field of psychiatry. This article proposes thus to explore the framework of personalized and precision medicine applied to psychiatry.
Methods
We explored the framework of personalized and precision medicine applied to psychiatry by a textual network analysis. Firstly, we performed a systematic text-mining (Natural Language Processing) from an exhaustive review of the international literature with the terms “precision psychiatry” and “personalized psychiatry”. Secondly, this analysis of textual data allowed us to build a textual network which made it possible to visualize the most proximal terms (the most frequently associated in the literature). Finally, we extracted from the network the main dimensions explored in the scientific literature, and we studied the relative importance of each term by analyzing the network centrality. In addition, a brief bibliometric analysis was conducted.
Results
We show that personalized and precision psychiatry refers to six dimensions found in the textual network analysis which correspond to the scientific fields which study personalized and precision psychiatry: genetics, pharmacogenetics, artificial intelligence, therapeutic trials, biomarkers and staging. We explore how each dimension relates to the mechanization of psychiatric disorders. However, precision and personalized psychiatry, which tries to refine the levels of mechanistic explanations for psychiatry, suffers from a conceptual heterogeneity. Indeed, textual analysis also allows us to find terms referring to a set of heterogeneous concepts. Many methodological fields and epistemological concepts are invoked in this literature, without standardization.
Conclusions
The paradox of personalized and precision psychiatry is to associate a strong conceptual heterogeneity with a well-defined mechanistic component. Heterogeneity found in literature on personalized and precision psychiatry testifies to the lack of a pluralist and integrative theoretical framework. This framework could be based on a naturalizing but non-reducing formalism, aware of the societal challenges of the sciences and their implementation in the research and clinical systems of psychiatry.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectifs. – La médecine personnalisée et de précision nécessite une clarification des concepts qui y sont rattachés. À notre connaissance, il n’existe pas d’exploration systématique de la littérature portant sur les dimensions et les concepts de la psychiatrie personnalisée et de précision et sur leurs usages dans les domaines neuroscientifiques et génétiques. Cet article propose donc d’explorer les dimensions et les concepts de la psychiatrie personnalisée et de précision.
Méthodes. – Une analyse en réseau par fouille de données textuelles systématique issue d’une revue exhaustive de la littérature internationale autour des termes de “precision psychiatry” et de “personalized psychiatry” a été réalisée. Cette fouille de données textuelles a été représentée sous forme d’un réseau permettant d’analyser les dimensions et les concepts de la psychiatrie personnalisée et de précision. Résultats. – La psychiatrie personnalisée et de précision renvoie à six dimensions retrouvées au sein de l’analyse du réseau textuel. Ces six dimensions correspondent aux domaines scientifiques qui étu- dient la psychiatrie personnalisée et de précision, à savoir : la génétique, la pharmacogénétique, les approches computationnelles, le raffinement des essais thérapeutiques, les biomarqueurs et la stadifica- tion. L’analyse des termes renvoie à un ensemble de concepts hétérogènes.
Conclusions. – L’hétérogénéité retrouvée dans la littérature sur la psychiatrie personnalisée et de précision peut témoigner d’un manque d’un cadre théorique pluraliste et intégratif. Ce cadre de travail pourrait être basé sur un formalisme naturalisant mais non réducteur, conscient des enjeux sociétaux des sciences et de leur implémentation dans les dispositifs de recherche et cliniques de la psychiatrie.
Objectives
The current challenges of psychiatric nosology and semiology are part of an interdisciplinary and integrative framework. The paradigm of the personalized and precision psychiatry proposes to study this discipline according to new approaches and methodologies. Personalized and precision psychiatry therefore requires clarification of its concepts. To our knowledge, there is no systematic exploration of the literature on the application of the concepts of personalized and precision medicine in the field of psychiatry. This article proposes thus to explore the framework of personalized and precision medicine applied to psychiatry.
Methods
We explored the framework of personalized and precision medicine applied to psychiatry by a textual network analysis. Firstly, we performed a systematic text-mining (Natural Language Processing) from an exhaustive review of the international literature with the terms “precision psychiatry” and “personalized psychiatry”. Secondly, this analysis of textual data allowed us to build a textual network which made it possible to visualize the most proximal terms (the most frequently associated in the literature). Finally, we extracted from the network the main dimensions explored in the scientific literature, and we studied the relative importance of each term by analyzing the network centrality. In addition, a brief bibliometric analysis was conducted.
Results
We show that personalized and precision psychiatry refers to six dimensions found in the textual network analysis which correspond to the scientific fields which study personalized and precision psychiatry: genetics, pharmacogenetics, artificial intelligence, therapeutic trials, biomarkers and staging. We explore how each dimension relates to the mechanization of psychiatric disorders. However, precision and personalized psychiatry, which tries to refine the levels of mechanistic explanations for psychiatry, suffers from a conceptual heterogeneity. Indeed, textual analysis also allows us to find terms referring to a set of heterogeneous concepts. Many methodological fields and epistemological concepts are invoked in this literature, without standardization.
Conclusions
The paradox of personalized and precision psychiatry is to associate a strong conceptual heterogeneity with a well-defined mechanistic component. Heterogeneity found in literature on personalized and precision psychiatry testifies to the lack of a pluralist and integrative theoretical framework. This framework could be based on a naturalizing but non-reducing formalism, aware of the societal challenges of the sciences and their implementation in the research and clinical systems of psychiatry.
Méthodes. – Une analyse en réseau par fouille de données textuelles systématique issue d’une revue exhaustive de la littérature internationale autour des termes de “precision psychiatry” et de “personalized psychiatry” a été réalisée. Cette fouille de données textuelles a été représentée sous forme d’un réseau permettant d’analyser les dimensions et les concepts de la psychiatrie personnalisée et de précision. Résultats. – La psychiatrie personnalisée et de précision renvoie à six dimensions retrouvées au sein de l’analyse du réseau textuel. Ces six dimensions correspondent aux domaines scientifiques qui étu- dient la psychiatrie personnalisée et de précision, à savoir : la génétique, la pharmacogénétique, les approches computationnelles, le raffinement des essais thérapeutiques, les biomarqueurs et la stadifica- tion. L’analyse des termes renvoie à un ensemble de concepts hétérogènes.
Conclusions. – L’hétérogénéité retrouvée dans la littérature sur la psychiatrie personnalisée et de précision peut témoigner d’un manque d’un cadre théorique pluraliste et intégratif. Ce cadre de travail pourrait être basé sur un formalisme naturalisant mais non réducteur, conscient des enjeux sociétaux des sciences et de leur implémentation dans les dispositifs de recherche et cliniques de la psychiatrie.
Objectives
The current challenges of psychiatric nosology and semiology are part of an interdisciplinary and integrative framework. The paradigm of the personalized and precision psychiatry proposes to study this discipline according to new approaches and methodologies. Personalized and precision psychiatry therefore requires clarification of its concepts. To our knowledge, there is no systematic exploration of the literature on the application of the concepts of personalized and precision medicine in the field of psychiatry. This article proposes thus to explore the framework of personalized and precision medicine applied to psychiatry.
Methods
We explored the framework of personalized and precision medicine applied to psychiatry by a textual network analysis. Firstly, we performed a systematic text-mining (Natural Language Processing) from an exhaustive review of the international literature with the terms “precision psychiatry” and “personalized psychiatry”. Secondly, this analysis of textual data allowed us to build a textual network which made it possible to visualize the most proximal terms (the most frequently associated in the literature). Finally, we extracted from the network the main dimensions explored in the scientific literature, and we studied the relative importance of each term by analyzing the network centrality. In addition, a brief bibliometric analysis was conducted.
Results
We show that personalized and precision psychiatry refers to six dimensions found in the textual network analysis which correspond to the scientific fields which study personalized and precision psychiatry: genetics, pharmacogenetics, artificial intelligence, therapeutic trials, biomarkers and staging. We explore how each dimension relates to the mechanization of psychiatric disorders. However, precision and personalized psychiatry, which tries to refine the levels of mechanistic explanations for psychiatry, suffers from a conceptual heterogeneity. Indeed, textual analysis also allows us to find terms referring to a set of heterogeneous concepts. Many methodological fields and epistemological concepts are invoked in this literature, without standardization.
Conclusions
The paradox of personalized and precision psychiatry is to associate a strong conceptual heterogeneity with a well-defined mechanistic component. Heterogeneity found in literature on personalized and precision psychiatry testifies to the lack of a pluralist and integrative theoretical framework. This framework could be based on a naturalizing but non-reducing formalism, aware of the societal challenges of the sciences and their implementation in the research and clinical systems of psychiatry.
2016
Proceedings Articles
22.
Rykov, Yuri; Nagornyy, Oleg; Koltsova, Olessia
Semantic and Geospatial Mapping of Instagram Images in Saint-Petersburg Proceedings Article
In: Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, pp. 75, 2016.
@inproceedings{rykov2016semantic,
title = {Semantic and Geospatial Mapping of Instagram Images in Saint-Petersburg},
author = {Yuri Rykov and Oleg Nagornyy and Olessia Koltsova},
url = {https://www.researchgate.net/publication/316664797_Semantic_and_Geospatial_Mapping_of_Instagram_Images_in_Saint-Petersburg},
year = {2016},
date = {2016-01-01},
booktitle = {Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference},
volume = {2607},
pages = {75},
abstract = {The availability of large urban social media data creates new opportunities for studying cities. In our paper we propose a new direction for this research: a joint analysis of geolocations of shared images and their content as determined by computer vision. To test our ideas, we use a dataset of 47,410 Instagram images shared in the city of St.Petersburg over one year. We show how a combination of semantic clustering, image recognition and geospatial analysis can detect important patterns related to both how people use a city and how they represent in social media.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The availability of large urban social media data creates new opportunities for studying cities. In our paper we propose a new direction for this research: a joint analysis of geolocations of shared images and their content as determined by computer vision. To test our ideas, we use a dataset of 47,410 Instagram images shared in the city of St.Petersburg over one year. We show how a combination of semantic clustering, image recognition and geospatial analysis can detect important patterns related to both how people use a city and how they represent in social media.
2014
Journal Articles
23.
Leydesdorff, Loet; Goldstone, Robert L
Interdisciplinarity at the journal and specialty level: The changing knowledge bases of the journal Cognitive Science Journal Article
In: Journal of the Association for Information Science and Technology, vol. 65, no. 1, pp. 164-177, 2014, (https://doi.org/10.1002/asi.22953).
@article{leydesdorff2014interdisciplinarity,
title = {Interdisciplinarity at the journal and specialty level: The changing knowledge bases of the journal Cognitive Science},
author = {Loet Leydesdorff and Robert L Goldstone},
url = {http://dx.doi.org/10.1002/asi.22953},
doi = {10.1002/asi.22953},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Journal of the Association for Information Science and Technology},
volume = {65},
number = {1},
pages = {164-177},
publisher = {Wiley Online Library},
abstract = {Using the referencing patterns in articles in Cognitive Science over three decades, we analyze the knowledge base of this literature in terms of its changing disciplinary composition. Three periods are distinguished: (A) construction of the interdisciplinary space in the 1980s, (B) development of an interdisciplinary orientation in the 1990s, and (C) reintegration into “cognitive psychology” in the 2000s. The fluidity and fuzziness of the interdisciplinary delineations in the different visualizations can be reduced and clarified using factor analysis. We also explore newly available routines (“CorText”) to analyze this development in terms of “tubes” using an alluvial map and compare the results with an animation (using “Visone”). The historical specificity of this development can be compared with the development of “artificial intelligence” into an integrated specialty during this same period. Interdisciplinarity should be defined differently at the level of journals and of specialties.},
note = {https://doi.org/10.1002/asi.22953},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Using the referencing patterns in articles in Cognitive Science over three decades, we analyze the knowledge base of this literature in terms of its changing disciplinary composition. Three periods are distinguished: (A) construction of the interdisciplinary space in the 1980s, (B) development of an interdisciplinary orientation in the 1990s, and (C) reintegration into “cognitive psychology” in the 2000s. The fluidity and fuzziness of the interdisciplinary delineations in the different visualizations can be reduced and clarified using factor analysis. We also explore newly available routines (“CorText”) to analyze this development in terms of “tubes” using an alluvial map and compare the results with an animation (using “Visone”). The historical specificity of this development can be compared with the development of “artificial intelligence” into an integrated specialty during this same period. Interdisciplinarity should be defined differently at the level of journals and of specialties.
LIST OF SCIENTIFIC WORKS THAT HAVE USED CORTEXT MANAGER
(Sources: Google Scholar, HAL, Scopus, WOS and search engines)
We are grateful that you have found CorTexT Manager useful. Over the years, you have been more than 1050 authors to trust CorTexT for your publicly accessible analyzes. This represents a little less than 10% of CorTexT Manager user’s community. So, thank you!
We seek to understand how the scientific production that used CorText Manager has evolved and to characterise it. You will find here our analysis of this scientific production.
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