1. AMOOZADEH M., DANIELS D., NAM D., KUMAR A., CHEN S., HILTON M. et al., 2024, Trust in Generative AI among students: An exploratory study, Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 67–73.
2. ARCILA B.B., 2023, Is It a Platform? Is It a Search Engine? It’s ChatGPT! The European Liability Regime for Large Language Models, J. Free Speech L., 3, 455.
3. BANSAL H., YIN D., MONAJATIPOOR M., CHANG K.W., 2022, How well can text-to-image generative models understand ethical natural language interventions? arXiv preprint, arXiv:2210.15230.
4. CHANG Y., WANG X., WANG J., WU Y., YANG L., ZHU K., et al., 2024, A survey on evaluation of large language models, ACM Transactions on Intelligent Systems and Technology, 15(3), 1–45.
5. COLLINGRIDGE D., 1982, The social control of technology, Francis Pinter, London.
6. DEPARTMENT FOR DIGITAL, CULTURE, MEDIA & SPORT, 2022, Establishing a pro-innovation approach to regulating AI, UK Government, https://www.gov.uk/government/publications/establishing-a-pro-innovation-approach-to-regulating-ai/establishing-a-pro-innovation-approach-to-regulating-ai-policy-statement.
7. DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY, 2023, A pro-innovation approach to AI regulation (CP 796), GOV.UK, https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper.
8. DJEFFAL C., 2020, Sustainable AI development (SAID): On the road to more access to justice, Technology, innovation and access to justice: Dialogues on the future of law, eds. De Souza S.P., Sphorm M., Edinburgh University Press, 112–130. https://doi.org/10.1515/9781474473880-013.
9. DUBEY S.R., SINGH S.K., 2024, Transformer-based generative adversarial networks in computer vision: A comprehensive survey, IEEE Transactions on Artificial Intelligence.
10. EUROPEAN COMMISSION, 2024, Artificial Intelligence Act, https://ec.europa.eu/digital-strategy/our-policies/artificial-intelligence_en.
11. EXECUTIVE OFFICE OF THE PRESIDENT, 2023, Executive Order on the Safe, Secure, and Trustworthy Devel-opment and Use of Artificial Intelligence, https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/27/fact-sheet-executive-order-on-artificial-intelligence/.
12. EXECUTIVE ORDER NO. 14110, 2023, 88 Fed. Reg. 75191 [hereinafter AI EO], at Sec. 2.
13. FOLKE C., HAHN T., OLSSON P., NORBERG J., 2005, Adaptive governance of social-ecological systems, Annu-al Review of Environment and Resources, 30: 441–473, https://doi.org/10.1146/annurev.energy.30.050504.144511.
14. GENERATIVE MODELS: VAEs, GANs, diffusion, transformers, NeRFs. https://www.techtarget.com/searchenterpriseai/tip/Generative-models-VAEs-GANs-diffusion-transformers-NeRFs.
15. HACKER P., ENGEL A., MAUER M., 2023, Regulating ChatGPT and other large generative AI models, Proceed-ings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 1112–1123, https://doi.org/10.48550/arXiv.2302.02337.
16. HACKER P., MITTELSTADT B., BORGESIUS F.Z., WACHTER S., 2024, Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It. arXiv preprint, arXiv:2407.10329.
17. JAMES W., 2023, AI art copyright lawsuit: Getty Images vs. Stable Diffusion, https://www.theverge.com/2023/2/6/23587393/ai-art-copyright-lawsuit-getty-images-stable-diffusion.
18. KASNECI E., SEßLER K., KÜCHEMANN S., BANNERT M., DEMENTIEVA D., FISCHER F. et al., 2023, ChatGPT for good? On opportunities and challenges of large language models for education, Learning and Individual Differences, 103: 102274.
19. LIN J.C., YOUNESSI D.N., KURAPATI S.S., TANG O.Y., SCOTT I.U., 2023, Comparison of GPT-3.5, GPT-4, and human user performance on a practice ophthalmology written examination, Eye, 37(17), 3694–3695.
20. NATIONAL TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION (NTIA), 2024, AI ac-countability policy report: Overview, https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report/overview.
21. OPENAI, 2023, GPT-4 Technical Report, https://cdn.openai.com/papers/gpt-4.pdf.
22. PAHUJA A., HASSAN R., CHANDEL A., KAUSHIK N., BHANOT N., FAREED A., 2025. Domain Development and Future Research Agendas in Corporate Governance and Sustainability Research: A Review, Sustainable Development, 0:1–22 https://doi.org/10.1002/sd.3559
23. RENN O., 2008, Risk governance: Coping with uncertainty in a complex world (2nd ed.), Earthscan.
24. SCHRAMOWSKI P., BRACK M., DEISEROTH B., KERSTING K., 2023, Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models, Proceedings of the IEEE/CVF Conference on Computer Vision and Pat-tern Recognition, 22522–22531.
25. SINGH A., OGUNFUNMI T., 2021, An overview of variational autoencoders for source separation, finance, and bio-signal applications, Entropy, 24(1), 55.
26. SMITS J., BORGHUIS T., 2022, Generative AI and intellectual property rights, Law and Artificial Intelligence: Regulating AI and Applying AI in Legal Practice, T.M.C. Asser Press, Hague, 323–344.
27. SOPHIA W., 2024, Artificial intelligence and artists’ intellectual property: Unpacking copyright infringement allegations in Andersen v. Stability AI Ltd., https://itsartlaw.org/2024/02/26/artificial-intelligence-and-artists-intellectual-property-unpacking-copyright-infringement-allegations-in-andersen-v-stability-ai-ltd/.
28. STATISTA MARKET INSIGHTS, 2024, Generative AI – worldwide. Statista, https://www.statista.com/outlook/tmo/artificial-intelligence/generative-ai/worldwide.
29. STRUBELL E., GANESH A., MCCALLUM A., 2019, Energy and policy considerations for deep learning in natural language processing, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650, https://doi.org/10.18653/v1/P19-1355.
30. STRUPPEK L., HINTERSDORF D., FRIEDRICH F., SCHRAMOWSKI P., KERSTING K., 2023, Exploiting cultural biases via homoglyphs in text-to-image synthesis, Journal of Artificial Intelligence Research, 78, 1017–1068.
31. TORTORA L., 2024, Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry, Frontiers in Psychiatry, 15, 1346059.
32. TRUBY J., 2020, Governing artificial intelligence to benefit the UN Sustainable Development Goals, Sustainable Development, 28(4), 946–959, https://doi.org/10.1002/sd.2048.
33. U.S. DEPARTMENT OF COMMERCE, 2024, Department of Commerce announces new actions to implement President Biden’s strategy, https://www.commerce.gov/news/press-releases/2024/04/department-commerce-announces-new-actions-implement-president-bidens.
34. UNITED NATIONS, 2015, The 2030 Agenda for Sustainable Development, https://sdgs.un.org/2030agenda.
35. VINUESA R., AZIZPOUR H., LEITE I., et al., 2020, The role of artificial intelligence in achieving the Sustainable Development Goals, Nature Communications 11(1), 233, https://doi.org/10.1038/s41467-019-14108-y.
36. WOLF M.J., MILLER K., GRODZINSKY F.S., 2017, Why we should have seen that coming: comments on Mi-crosoft’s Tay experiment and wider implications, ACM SIGCAS Computers and Society 47(3), 54–64.
37. YANG L., ZHANG Z., SONG Y., HONG S., XU R., ZHAO Y. et al., 2023, Diffusion models: A comprehensive survey of methods and applications, ACM Computing Surveys 56(4), 1–39.
38. ZHANG P., KAMEL BOULOS M.N., 2023, Generative AI in medicine and healthcare: promises, opportunities and challenges, Future Internet 15(9), 286.
39. ZHOU C., LI Q., LI C., YU J., LIU Y., WANG G. et al., 2023, A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT, arXiv preprint, arXiv:2302.09419.
40. ZHOU M., ABHISHEK V., DERDENGER T., KIM J., SRINIVASAN K., 2024, Bias in generative AI, arXiv preprint, arXiv:2403.02726.