pith. sign in

arxiv: 2606.23094 · v1 · pith:HGHX56PDnew · submitted 2026-06-22 · 💻 cs.AI · cs.CL

Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind

Vamshi Krishna Bonagiri , Juan Nicolas Sepulveda-Arias , Abdoul Jalil Djiberou Mahamadou , Monojit Choudhury This is my paper

Pith reviewed 2026-06-26 08:45 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords cognitive digital twinsAI governanceethical riskscognitive representationproxy action5A frameworkmind modelingAI ethics
0
0 comments X

The pith

Cognitive digital twins require governance at the level of cognitive representation itself, before any final decision or external action occurs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines cognitive digital twins as dynamic computational representations of a specific person's cognition, updated from behavioral, contextual, or physiological data to model, predict, simulate, or serve as a communicative or decision-making proxy. It argues that the combination of cognitive inference with longitudinal representation, simulation, and proxy action creates risks and governance needs that existing frameworks for data processing, automated decisions, or autonomous agents only partially address. The authors introduce a 5A governance framework organized around authority, autonomy, access and control, accountability, and availability. They identify CDT-specific risks including misrepresentation, epistemic authority shifts, shadow twins, simulated participation, proxy action, and proxy-power asymmetries. They propose requirements for high-risk CDTs that strengthen consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement.

Core claim

CDTs combine cognitive inference with longitudinal representation, simulation, and proxy action in ways that existing governance strategies only partially address. Existing frameworks primarily regulate data processing, automated decisions, or autonomous actions; CDTs also require governance at the level of cognitive representation itself. CDTs can become infrastructures through which cognition is represented, simulated, classified, and operationalized, so they need rules not only because they can act for people but because of how they model minds.

What carries the argument

The 5A governance framework organized around authority, autonomy, access and control, accountability, and availability, which targets risks arising from the specific combination of cognitive inference, longitudinal representation, simulation, and proxy action.

If this is right

  • High-risk CDTs require strengthened consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement.
  • Governance must target the cognitive representation layer itself, not only downstream data handling or final actions.
  • CDTs introduce distinct risks from misrepresentation, epistemic authority shifts, shadow twins, simulated participation, proxy action, and proxy-power asymmetries that adjacent systems do not create in the same way.
  • CDTs must be distinguished from personal assistants, autonomous agents, recommender systems, and automated decision systems because their longitudinal cognitive modeling adds new layers of representation and simulation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The 5A framework might be applied to evaluate whether current personal AI assistants already cross into CDT territory through persistent user modeling.
  • Regulators could test the framework by auditing whether existing consent mechanisms capture the representational aspects of systems that simulate user cognition over time.
  • If the claim holds, new standards for model retirement could become relevant for any AI that maintains a persistent cognitive profile of an individual.
  • The distinction between CDTs and other systems could inform updates to data protection laws that currently focus on personal data rather than cognitive proxies.

Load-bearing premise

The specific combination of cognitive inference, longitudinal representation, simulation, and proxy action in CDTs creates governance needs that cannot be met by extending existing strategies for personal assistants, autonomous agents, recommender systems, and automated decision systems.

What would settle it

A demonstration that all CDT risks listed in the paper, including misrepresentation and proxy-power asymmetries, are already fully addressed by current regulations on data processing, automated decisions, and autonomous actions without additional rules at the representation level.

read the original abstract

As AI systems become increasingly persistent and personalized, they make possible a class of technologies that we call cognitive digital twins (CDTs): dynamic computational representations of a specific person's cognition, updated from behavioral, contextual, or physiological data in order to model, predict, or simulate that person's cognition, or to act as that person's communicative or decision-making proxy. CDTs combine cognitive inference with longitudinal representation, simulation, and proxy action in ways that existing governance strategies for personal assistants, autonomous agents, recommender systems, and automated decision systems only partially address. This paper makes four contributions. First, we define CDTs and distinguish them from adjacent systems. Second, we introduce a 5A governance framework organized around authority, autonomy, access and control, accountability, and availability. Third, we identify CDT-specific risks, from misrepresentation and epistemic authority shifts to shadow twins, simulated participation, proxy action, and proxy-power asymmetries. Fourth, we analyze governance gaps and propose requirements for high-risk CDTs that strengthen consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement. Existing frameworks primarily regulate data processing, automated decisions, or autonomous actions; CDTs also require governance at the level of cognitive representation itself, before any final decision or external action occurs. We argue that CDTs require governance not only because they can act for people, but because they can become infrastructures through which cognition is represented, simulated, classified, and operationalized.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript defines Cognitive Digital Twins (CDTs) as dynamic computational representations of a specific person's cognition, updated from behavioral, contextual, or physiological data to model, predict, simulate, or act as a communicative/decision-making proxy. It distinguishes CDTs from adjacent systems, introduces a 5A governance framework (authority, autonomy, access and control, accountability, availability), enumerates CDT-specific risks (misrepresentation, epistemic authority shifts, shadow twins, simulated participation, proxy action, proxy-power asymmetries), and proposes high-risk requirements (strengthened consent, purpose limitation, validity, traceability, contestation, independent review, model retirement). The central normative claim is that existing frameworks regulating data processing, automated decisions, or autonomous actions only partially address CDTs, which also require governance at the level of cognitive representation itself.

Significance. If the distinctions and risk taxonomy hold, the paper contributes a structured framework for addressing representational harms in personalized AI, potentially informing extensions to regulations like the EU AI Act. It explicitly credits the enumeration of pre-action risks and the call for representation-level rules as advancing beyond data-centric or action-centric governance.

major comments (1)
  1. [Abstract (contributions paragraph)] Abstract (contributions paragraph): The assertion that CDT governance needs 'cannot be met by extending existing strategies' for personal assistants, autonomous agents, recommender systems, and automated decision systems is load-bearing for the central claim but rests on definitional distinction and risk listing without a systematic mapping showing why extensions (e.g., of purpose limitation or contestation mechanisms) would fail to cover longitudinal cognitive simulation and proxy representation.
minor comments (1)
  1. The 5A framework is outlined at a high level; adding one or two worked examples of how authority and accountability would apply to a specific CDT use case (e.g., in the governance gaps section) would improve clarity without altering the argument.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading and for identifying this load-bearing claim in the abstract. The comment correctly notes that the argument for representation-level governance rests on the definitional distinctions and risk taxonomy. We address the point directly below and indicate where the manuscript will be revised to make the reasoning more systematic.

read point-by-point responses

  1. Referee: [Abstract (contributions paragraph)] Abstract (contributions paragraph): The assertion that CDT governance needs 'cannot be met by extending existing strategies' for personal assistants, autonomous agents, recommender systems, and automated decision systems is load-bearing for the central claim but rests on definitional distinction and risk listing without a systematic mapping showing why extensions (e.g., of purpose limitation or contestation mechanisms) would fail to cover longitudinal cognitive simulation and proxy representation.

    Authors: The manuscript grounds the claim in two elements: (1) the definition of CDTs as longitudinal cognitive representations that can operate as proxies before any external action occurs, and (2) the enumeration of risks (misrepresentation, epistemic authority shifts, shadow twins, simulated participation, proxy action, and proxy-power asymmetries) that arise specifically at the level of cognitive modeling rather than data processing or final decisions. Existing frameworks such as GDPR purpose limitation or the EU AI Act’s requirements for automated decision-making primarily constrain inputs or outputs; they do not directly regulate the ongoing construction and simulation of an individual’s cognitive profile itself. Nevertheless, the referee is correct that a more explicit mapping would strengthen the argument. We will therefore add a new subsection (provisionally 4.3) containing a table that systematically compares each CDT-specific risk against the coverage provided by purpose limitation, contestation rights, and high-risk AI obligations under current regimes, showing the residual gaps at the representational layer. This revision will make the abstract claim more fully supported without altering the core thesis. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a normative and conceptual proposal that defines cognitive digital twins, distinguishes them from adjacent systems, enumerates risks, and advances a 5A governance framework plus high-risk requirements. No equations, derivations, fitted parameters, or technical models are present whose validity would collapse by construction to prior inputs or self-citations. The central claim—that the combination of cognitive inference, longitudinal representation, simulation, and proxy action creates governance needs not fully met by extending existing strategies—is advanced through definitional analysis and risk taxonomy rather than any self-referential reduction or imported uniqueness theorem. Self-citations, if present, do not carry the load-bearing argument.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claims rest on domain assumptions about the uniqueness of CDT risks and the insufficiency of existing governance; no free parameters or formal mathematical axioms are used.

axioms (1)
  • domain assumption Existing governance strategies for personal assistants, autonomous agents, recommender systems, and automated decision systems only partially address CDTs.
    Directly stated in the abstract as the premise motivating the new framework and risk analysis.
invented entities (1)
  • Cognitive Digital Twins (CDTs) no independent evidence
    purpose: To name and distinguish a class of AI systems that combine cognitive inference with longitudinal representation, simulation, and proxy action.
    New term and category introduced in the paper to organize the governance discussion.

pith-pipeline@v0.9.1-grok · 5814 in / 1369 out tokens · 35572 ms · 2026-06-26T08:45:36.652919+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

76 extracted references · 1 canonical work pages

  1. [1]

    Frontiers in Genetics , volume =

    Bruynseels, Koen and Santoni de Sio, Filippo and van den Hoven, Jeroen , title =. Frontiers in Genetics , volume =. 2018 , doi =

    2018

  2. [2]

    Journal of Medical Ethics , volume =

    Braun, Matthias , title =. Journal of Medical Ethics , volume =. 2021 , doi =

    2021

  3. [3]

    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume =

    de Kerckhove, Derrick , title =. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume =. 2021 , doi =

    2021

  4. [4]

    Journal of Cloud Computing , volume =

    Lin, Yujia and Chen, Li-Ming and Ali, Aftab and Nugent, Christopher and Cleland, Ian and Li, Rongyang and Ding, Jianguo and Ning, Huansheng , title =. Journal of Cloud Computing , volume =. 2024 , doi =

    2024

  5. [5]

    npj Digital Medicine , volume =

    Katsoulakis, Evangelia and Wang, Qi and Wu, Huanmei and Shahriyari, Leili and Fletcher, Richard and Liu, Jinwei and Achenie, Luke and Liu, Hongfang and Jackson, Pamela and Xiao, Ying and Syeda-Mahmood, Tanveer and Tuli, Richard and Deng, Jun , title =. npj Digital Medicine , volume =. 2024 , doi =

    2024

  6. [6]

    , title =

    Onnela, Jukka-Pekka and Rauch, Scott L. , title =. Neuropsychopharmacology , volume =. 2016 , doi =

    2016

  7. [7]

    and Lorme, Jeanette and Onnela, Jukka-Pekka , title =

    Torous, John and Kiang, Mathew V. and Lorme, Jeanette and Onnela, Jukka-Pekka , title =. JMIR Mental Health , volume =. 2016 , doi =

    2016

  8. [8]

    and Zhang, Mi and Schueller, Stephen M

    Mohr, David C. and Zhang, Mi and Schueller, Stephen M. , title =. Annual Review of Clinical Psychology , volume =. 2017 , doi =

    2017

  9. [9]

    and Cho, Mildred K

    Martinez-Martin, Nicole and Greely, Henry T. and Cho, Mildred K. , title =. JMIR mHealth and uHealth , volume =. 2021 , doi =

    2021

  10. [10]

    and Littlejohn, Kaylo T

    Metzger, Sean L. and Littlejohn, Kaylo T. and Silva, Alexander B. and Moses, David A. and Seaton, Margaret P. and Wang, Ran and Dougherty, Margaret E. and Liu, Jessie R. and Wu, Peter and Berger, Michael A. and Zhuravleva, Inga and Tu-Chan, An and Ganguly, Karunesh and Anumanchipalli, Gopala K. and Chang, Edward F. , title =. Nature , volume =. 2023 , doi =

    2023

  11. [11]

    and Song, Dong and Robinson, Brett S

    Hampson, Robert E. and Song, Dong and Robinson, Brett S. and Fetterhoff, Dennis and Dakos, Andrew S. and Roeder, Brian M. and She, Xiayu and Wicks, Robert T. and Witcher, Matthew R. and Couture, Daniel C. and Laxton, Adrian W. and Munger-Clary, Heidi and Popli, Gaurav and Sollman, Matthew J. and Whitlow, Christopher T. and Marmarelis, Vasilis Z. and Berge...

    2018

  12. [12]

    Philosophy & Technology , volume =

    Hollanek, Tomasz and Nowaczyk-Basinska, Katarzyna , title =. Philosophy & Technology , volume =. 2024 , doi =

    2024

  13. [13]

    2026 , url =

    Where Your. 2026 , url =

    2026

  14. [14]

    and Cai, Carrie J

    Park, Joon Sung and O'Brien, Joseph C. and Cai, Carrie J. and Morris, Meredith Ringel and Liang, Percy and Bernstein, Michael S. , title =. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology , pages =. 2023 , publisher =

    2023

  15. [15]

    and Shaw, Aaron and Hill, Benjamin Mako and Cai, Carrie J

    Park, Joon Sung and Zou, Carolyn Q. and Shaw, Aaron and Hill, Benjamin Mako and Cai, Carrie J. and Morris, Meredith Ringel and Willer, Robb and Liang, Percy and Bernstein, Michael S. , title =. arXiv preprint arXiv:2411.10109 , year =

    Pith/arXiv arXiv

  16. [16]

    Proceedings of the 40th International Conference on Machine Learning , pages =

    Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies , author =. Proceedings of the 40th International Conference on Machine Learning , pages =. 2023 , editor =

    2023

  17. [17]

    and Busby, Ethan C

    Argyle, Lisa P. and Busby, Ethan C. and Fulda, Nancy and Gubler, Joshua R. and Rytting, Christopher and Wingate, David , title =. Political Analysis , volume =. 2023 , doi =

    2023

  18. [18]

    Regulation (EU) 2024/1689 Laying Down Harmonised Rules on Artificial Intelligence , year =

    2024

  19. [19]

    Regulation (EU) 2016/679, General Data Protection Regulation , year =

    2016

  20. [20]

    2023 , doi =

    Artificial Intelligence Risk Management Framework (AI RMF 1.0) , institution =. 2023 , doi =

    2023

  21. [21]

    Baumer, Eric P. S. and McGee, Micki , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  22. [22]

    Proceedings of the 42nd International Conference on Machine Learning , pages =

    Position: AI Agents Need Authenticated Delegation , author =. Proceedings of the 42nd International Conference on Machine Learning , pages =. 2025 , editor =

    2025

  23. [23]

    and Huey, Joanna and Barocas, Solon and Felten, Edward W

    Kroll, Joshua A. and Huey, Joanna and Barocas, Solon and Felten, Edward W. and Reidenberg, Joel R. and Robinson, David G. and Yu, Harlan , title =. University of Pennsylvania Law Review , volume =. 2017 , url =

    2017

  24. [24]

    and Bello, Paul and Scheutz, Matthias , title =

    Malle, Bertram F. and Bello, Paul and Scheutz, Matthias , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  25. [25]

    arXiv preprint arXiv:1606.06565 , year =

    Amodei, Dario and Olah, Chris and Steinhardt, Jacob and Christiano, Paul and Schulman, John and Mane, Dan , title =. arXiv preprint arXiv:1606.06565 , year =

    Pith/arXiv arXiv

  26. [26]

    2022 , eprint =

    Carlsmith, Joseph , title =. 2022 , eprint =

    2022

  27. [27]

    Power-Seeking Can Be Probable and Predictive for Trained Agents , year =

    Krakovna, Victoria and Kram. Power-Seeking Can Be Probable and Predictive for Trained Agents , year =. 2304.06528 , archivePrefix =

    arXiv

  28. [28]

    Nissenbaum, Helen , title =

  29. [29]

    Columbia Business Law Review , volume =

    Wachter, Sandra and Mittelstadt, Brent , title =. Columbia Business Law Review , volume =. 2019 , url =

    2019

  30. [30]

    , title =

    Thompson, Dennis F. , title =. American Political Science Review , volume =. 1980 , doi =

    1980

  31. [31]

    Ethics and Information Technology , volume =

    Matthias, Andreas , title =. Ethics and Information Technology , volume =. 2004 , doi =

    2004

  32. [32]

    Engaging Science, Technology, and Society , volume =

    Elish, Madeleine Clare , title =. Engaging Science, Technology, and Society , volume =. 2019 , doi =

    2019

  33. [33]

    Boston College Law Review , volume =

    Crawford, Kate and Schultz, Jason , title =. Boston College Law Review , volume =. 2014 , url =

    2014

  34. [34]

    and Mitchell, Margaret and Gebru, Timnit and Hutchinson, Ben and Smith-Loud, Jamila and Theron, Daniel and Barnes, Parker , title =

    Raji, Inioluwa Deborah and Smart, Andrew and White, Rebecca N. and Mitchell, Margaret and Gebru, Timnit and Hutchinson, Ben and Smith-Loud, Jamila and Theron, Daniel and Barnes, Parker , title =. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , pages =. 2020 , publisher =

    2020

  35. [35]

    Eubanks, Virginia , title =

  36. [36]

    and Valentine, Melissa A

    Kellogg, Katherine C. and Valentine, Melissa A. and Christin, Angele , title =. Academy of Management Annals , volume =. 2020 , doi =

    2020

  37. [37]

    International Journal of Communication , volume =

    Rosenblat, Alex and Stark, Luke , title =. International Journal of Communication , volume =. 2016 , url =

    2016

  38. [38]

    Proceedings of the 42nd International Conference on Machine Learning , pages =

    Position: Humanity Faces Existential Risk from Gradual Disempowerment , author =. Proceedings of the 42nd International Conference on Machine Learning , pages =. 2025 , editor =

    2025

  39. [39]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume =

    Xu, Jiannan and Li, Gujie and Jiang, Jane Yi , title =. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume =. 2025 , doi =

    2025

  40. [40]

    , title =

    Andrus, McKane and Gilbert, Thomas K. , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  41. [41]

    and Wallach, Hanna , title =

    Jacobs, Abigail Z. and Wallach, Hanna , title =. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , pages =. 2021 , publisher =

    2021

  42. [42]

    Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization , pages =

    Suresh, Harini and Guttag, John , title =. Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization , pages =. 2021 , publisher =

    2021

  43. [43]

    Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Jiang, Ray and Chiappa, Silvia and Lattimore, Tor and Gyorgy, Andras and Kohli, Pushmeet , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  44. [44]

    Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Cruz Cortes, Efren and Ghosh, Debashis , title =. Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2020 , publisher =

    2020

  45. [45]

    Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =

    Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit , title =. Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =. 2019 , publisher =

    2019

  46. [46]

    Communications of the ACM , volume =

    Gebru, Timnit and Morgenstern, Jamie and Vecchione, Briana and Vaughan, Jennifer Wortman and Wallach, Hanna and Daume III, Hal and Crawford, Kate , title =. Communications of the ACM , volume =. 2021 , doi =

    2021

  47. [47]

    Human Factors , volume =

    Parasuraman, Raja and Riley, Victor , title =. Human Factors , volume =. 1997 , doi =

    1997

  48. [48]

    Analysis , volume =

    Clark, Andy and Chalmers, David , title =. Analysis , volume =. 1998 , doi =

    1998

  49. [49]

    Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Hernandez-Orallo, Jose and Vold, Karina , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  50. [50]

    and Gilbert, Sam J

    Risko, Evan F. and Gilbert, Sam J. , title =. Trends in Cognitive Sciences , volume =. 2016 , doi =

    2016

  51. [51]

    Fricker, Miranda , title =

  52. [52]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume =

    Kay, Jackie and Kasirzadeh, Atoosa and Mohamed, Shakir , title =. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume =. 2024 , doi =

    2024

  53. [53]

    , title =

    Merton, Robert K. , title =. The Antioch Review , volume =. 1948 , doi =

    1948

  54. [54]

    and Zrnic, Tijana and Mendler-Dunner, Celestine and Hardt, Moritz , title =

    Perdomo, Juan C. and Zrnic, Tijana and Mendler-Dunner, Celestine and Hardt, Moritz , title =. Proceedings of the 37th International Conference on Machine Learning , pages =. 2020 , volume =

    2020

  55. [55]

    and Mertens, Marjolein , title =

    King, Oliver C. and Mertens, Marjolein , title =. Ethical Theory and Moral Practice , volume =. 2023 , doi =

    2023

  56. [56]

    Zuboff, Shoshana , title =

  57. [57]

    Privacy, Big Data, and the Public Good: Frameworks for Engagement , editor =

    Barocas, Solon and Nissenbaum, Helen , title =. Privacy, Big Data, and the Public Good: Frameworks for Engagement , editor =. 2014 , doi =

    2014

  58. [58]

    Georgetown Law Technology Review , volume =

    Susser, Daniel and Roessler, Beate and Nissenbaum, Helen , title =. Georgetown Law Technology Review , volume =. 2019 , url =

    2019

  59. [59]

    Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Susser, Daniel , title =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =. 2019 , publisher =

    2019

  60. [60]

    George Washington Law Review , volume =

    Calo, Ryan , title =. George Washington Law Review , volume =. 2014 , url =

    2014

  61. [61]

    and Kosinski, Michal and Nave, Gideon and Stillwell, David J

    Matz, Sandra C. and Kosinski, Michal and Nave, Gideon and Stillwell, David J. , title =. Proceedings of the National Academy of Sciences of the United States of America , volume =. 2017 , doi =

    2017

  62. [62]

    and Teeny, Jacob D

    Matz, Sandra C. and Teeny, Jacob D. and Vaid, Sumer S. and Peters, H. and Harari, Gabriella M. and Cerf, Moran , title =. Scientific Reports , volume =. 2024 , doi =

    2024

  63. [63]

    Proceedings of the National Academy of Sciences of the United States of America , volume =

    Hackenburg, Kobi and Margetts, Helen , title =. Proceedings of the National Academy of Sciences of the United States of America , volume =. 2024 , doi =

    2024

  64. [64]

    and Sastry, Girish and Musser, Micah and DiResta, Ren

    Goldstein, Josh A. and Sastry, Girish and Musser, Micah and DiResta, Ren. Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations , institution =. 2023 , url =

    2023

  65. [65]

    ACM Computing Surveys , year =

    Mou, Xinyi and Ding, Xuanwen and He, Qi and Wang, Liang and Liang, Jingcong and Zhang, Xinnong and Sun, Libo and Lin, Jiayu and Zhou, Jie and Huang, Xuanjing and Wei, Zhongyu , title =. ACM Computing Surveys , year =. doi:10.1145/3800683 , note =

    work page doi:10.1145/3800683

  66. [66]

    Humanities and Social Sciences Communications , volume =

    Gao, Chen and Lan, Xiaochong and Li, Nian and Yuan, Yuan and Ding, Jingtao and Zhou, Zhilun and Xu, Fengli and Li, Yong , title =. Humanities and Social Sciences Communications , volume =. 2024 , doi =

    2024

  67. [67]

    1979 , url =

    The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research , institution =. 1979 , url =

    1979

  68. [68]

    2012 , url =

    Dittrich, David and Kenneally, Erin , title =. 2012 , url =

    2012

  69. [69]

    arXiv preprint arXiv:1702.08608 , year =

    Doshi-Velez, Finale and Kim, Been , title =. arXiv preprint arXiv:1702.08608 , year =

    Pith/arXiv arXiv

  70. [70]

    Nature Machine Intelligence , volume =

    Rudin, Cynthia , title =. Nature Machine Intelligence , volume =. 2019 , doi =

    2019

  71. [71]

    Transactions on Machine Learning Research , year =

    Bereska, Leonard and Gavves, Stratis , title =. Transactions on Machine Learning Research , year =

  72. [72]

    and Lund, David and Morrison, Michael and Teare, Harriet and Melham, Karen , title =

    Kaye, Jane and Whitley, Edgar A. and Lund, David and Morrison, Michael and Teare, Harriet and Melham, Karen , title =. European Journal of Human Genetics , volume =. 2015 , doi =

    2015

  73. [73]

    and Kaye, Jane and Dixon, William G

    Williams, Harriet and Spencer, Karen and Sanders, Caroline and Lund, David and Whitley, Edgar A. and Kaye, Jane and Dixon, William G. , title =. JMIR Medical Informatics , volume =. 2015 , doi =

    2015

  74. [74]

    Life Sciences, Society and Policy , volume =

    Ienca, Marcello and Andorno, Roberto , title =. Life Sciences, Society and Policy , volume =. 2017 , doi =

    2017

  75. [75]

    and Fins, Joseph J

    Ligthart, Sjors and Ienca, Marcello and Meynen, Gerben and Molnar-Gabor, Fruzsina and Andorno, Roberto and Bublitz, Christoph and Catley, Paul and Claydon, Lisa and Douglas, Thomas and Farahany, Nita A. and Fins, Joseph J. and Goering, Sara and Haselager, Pim and Jotterand, Fabrice and Lavazza, Andrea and McCay, Allan and Wajnerman Paz, Abel and Rainey, S...

    2023

  76. [76]

    , title =

    Magee, Patrick and Ienca, Marcello and Farahany, Nita A. , title =. Neuron , volume =. 2024 , doi =

    2024