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A principled governance for emerging AI regimes: lessons from China, the European Union, and the United States

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Abstract

Artificial intelligence (AI) governance is anticipated to have a transformative impact on humanity which has prompted researchers to analyze its implementation and use to ensure that the technology advances ethically and is beneficial for society. Though countries have begun to develop governance initiatives to regulate AI, the number of emerging AI regimes with an established structure is still relatively low. Meanwhile, the technology is advancing rapidly and has already caused harm inequitably to underrepresented communities. Thus, there is an urgent need to establish robust governance to mitigate the issues and risks attendant when deploying AI.While numerous ethics, principles, and structures have been recommended, this article intends to address the policy lag by providing policymakers with a simple and compelling AI governance framework that situates AI principles as the guiding baseline for developing and evaluating policies. Rather than devising new policy recommendations, the most recent (at the time of writing) and comprehensive governance documents from China, the European Union, and the United States were systematically selected, and examined in a comparative analysis to study how the three regimes address AI principles. Based on the comparative analysis, the most comprehensive and effective recommendations were selected to produce seven broad policy recommendations. The governance framework and recommendations are intentionally broad so that they can be adapted to adequately address AI principles across diverse contexts, encouraging the implementation of AI principles, increasing the likelihood of beneficial AI, and reducing the risks and harms associated with the technology. Nevertheless, the recommendations provided should not be considered exhaustive as the technology has an immense reach and new AI governance initiatives are developing continuously in this growth period in AI governance. It is thus essential for policymakers to survey the most current and relevant governance landscape to identify the best practices that are suitable for their specific context and need.

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Notes

  1. Even though China’s PIPL has clear provisions regarding how individuals and organizations handle the means of data processing, it is unclear how the specific provisions for the Chinese state government will impact user data protection [30].

References

  1. Zhang, D., Maslej, N., Brynjolfsson, E., et al.: The AI Index 2022 Annual Report (2022).

  2. Taeihagh, A.: Governance of artificial intelligence. Policy Soc. 40, 137–157 (2021). https://doi.org/10.1080/14494035.2021.1928377

    Article  Google Scholar 

  3. Bostrom, N.: Superintelligence: Paths, Dangers, Strategies. Oxford University Press, Oxford (2014)

    Google Scholar 

  4. Brynjolfsson, E., McAfee, A.: The Second Machine Age, 1st edn. Norton Paperback, New York (2016)

    Google Scholar 

  5. Cath, C.: Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 376, 20180080 (2018). https://doi.org/10.1098/rsta.2018.0080

    Article  Google Scholar 

  6. Fjeld, J., Achten, N., Hilligoss, H., et al.: Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3518482

    Article  Google Scholar 

  7. Jobin, A., Ienca, M., Vayena, E.: The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2

    Article  Google Scholar 

  8. van Berkel, N., Papachristos, E., Giachanou, A., et al.: A systematic assessment of national artificial intelligence policies: perspectives from the nordics and beyond. In: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. ACM, Tallinn Estonia, pp 1–12 (2020).

  9. Raji, I.D., Smart, A., White, R.N., et al.: Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. 12 (2020).

  10. Floridi, L., Cowls, J.: A unified framework of five principles for AI in society. Harv. Data Sci. Rev. (2019). https://doi.org/10.1162/99608f92.8cd550d1

    Article  Google Scholar 

  11. Greene, D., Hoffmann, A.L., Stark, L.: Better, nicer, clearer, fairer: a critical assessment of the movement for ethical artificial intelligence and machine learning. In: Proceedings of 52nd Hawaii International Conference System Science 10 (2019).

  12. Zeng, Y., Lu, E., Ruan, Z.: Linking Artificial Intelligence Principles (LAIP). In: Link. AI Princ. LAIP. https://www.linking-ai-principles.org/keywords (2022). Accessed 19 Feb 2022.

  13. Zeng, Y., Lu, E., Huangfu, C.: Linking Artificial Intelligence Principles. ArXiv181204814 Cs (2018).

  14. Dick, S.A.: Making up minds. In: Think. Mach. Hist. Present Future AI. https://thinking-machines.online/dick/ (2021). Accessed 4 Dec 2021.

  15. Calo, R.: Artificial Intelligence Policy: A Primer and Roadmap. Social Science Research Network, Rochester (2017)

    Google Scholar 

  16. Lynch, S., Andrew, N.: Why ai is the new electricity. In: Stanf. Grad. Sch. Bus. https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity (2017). Accessed 11 Mar 2022.

  17. United Nations General Assembly.: Transforming our world: the 2030 agenda for sustainable development. In: U. N. https://sdgs.un.org/goals (2015). Accessed 19 Feb 2022.

  18. Gasser, U.: AI and the law: setting the stage. In: Berkman Klein Cent. Collect. https://medium.com/berkman-klein-center/ai-and-the-law-setting-the-stage-48516fda1b11 (2017). Accessed 25 Jan 2022.

  19. Castro, D., McLaughlin, M.: Who is winning the AI race: China, The EU, or the United States? 2021 Update. Center for Data Innovation (2021).

  20. Bal, R., Gill, I.S.: Policy Approaches to Artificial Intelligence Based Technologies in China, European Union and the United States. Social Science Research Network, Rochester (2020)

    Google Scholar 

  21. Daly, A., Hagendorff, T., Li, H., et al.: AI, Governance and Ethics: Global Perspectives. Social Science Research Network, Rochester (2020)

    Google Scholar 

  22. Schumer, C.E.: S.1260—117th Congress (2021–2022): United States Innovation and Competition Act of 2021 (2021).

  23. European Commission.: Proposal For A Regulation Of The European Parliament And Of The Council Laying Down Harmonised Rules On Artificial Intelligence (Artificial Intelligence Act) And Amending Certain Union Legislative Acts (2021).

  24. The National New Generation Artificial Intelligence Governance Specialist Committee.: Ethical norms for new generation artificial intelligence released. In: Center for Emerging Network Technologies. https://cset.georgetown.edu/publication/ethical-norms-for-new-generation-artificial-intelligence-released/ (2021). Accessed 25 Jan 2022.

  25. Vought, R.T.: Guidance for Regulation of Artificial Intelligence Applications. Office of Management and Budget, Washington (2020)

    Google Scholar 

  26. Circiumaru, A.: Three proposals to strengthen the EU Artificial Intelligence Act. In: Ada Lovelace Inst. https://www.adalovelaceinstitute.org/blog/three-proposals-strengthen-eu-artificial-intelligence-act/ (2021). Accessed 7 Mar 2022.

  27. Allen, J.R., West, D.M.: How artificial intelligence is transforming the world. In: Brookings. https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/ (2018). Accessed 14 Feb 2022.

  28. Armstrong, S., Bostrom, N., Shulman, C.: Racing to the precipice: a model of artificial intelligence development. AI Soc. 31, 201–206 (2016). https://doi.org/10.1007/s00146-015-0590-y

    Article  Google Scholar 

  29. McGregor, L., Murray, D., Ng, V.: International human rights law as a framework for algorithmic accountability. Int. Comp. Law Q. 68, 309–343 (2019). https://doi.org/10.1017/S0020589319000046

    Article  Google Scholar 

  30. Lee, A., Shi, M., Chen, Q., et al.: Seven major changes in China’s finalized personal information protection law. In: DigiChina. https://digichina.stanford.edu/work/seven-major-changes-in-chinas-finalized-personal-information-protection-law/ (2021). Accessed 13 Mar 2022.

  31. McGeveran, W.: Privacy and Data Protection Law. Foundation Press, Mineola (2016)

    Google Scholar 

  32. Li, Y., Kobsa, A., Knijnenburg, B.P., Carolyn Nguyen, M.-H.: Cross-cultural privacy prediction. Proc. Priv. Enhanc. Technol. 2017, 113–132 (2017). https://doi.org/10.1515/popets-2017-0019

    Article  Google Scholar 

  33. UNESCO.: Report of the Social and Human Sciences Commission (SHS). UNESCO (2021).

  34. Shneiderman, B.: Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Trans. Interact Intell. Syst. 10, 261–2631 (2020). https://doi.org/10.1145/3419764

    Article  Google Scholar 

  35. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias. In: ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing (2016). Accessed 31 Mar 2021.

  36. Noble, S.U.: Algorithms of Oppression. NYU Press, New York (2018)

    Book  Google Scholar 

  37. Bostrom, N., Dafoe, A., Flynn, C.: Public policy and superintelligent AI: a vector field approach. 28 (2018)

  38. Brundage, M., Avin, S., Clark, J., et al.: The malicious use of artificial intelligence: forecasting, prevention, and mitigation. ArXiv180207228 Cs (2018)

  39. Hamilton, I.A.: The FTC can move forward with its bid to make Meta sell Instagram and WhatsApp, judge rules. In: Bus. Insid. https://www.businessinsider.com/ruling-ftc-meta-facebook-lawsuit-instagram-whatsapp-can-proceed-2022-1 (2022). Accessed 15 Apr 2022.

  40. Frey, C.B., Osborne, M.A.: The future of employment: how susceptible are jobs to computerisation? Technol. Forecast Soc. Change 114, 254–280 (2017). https://doi.org/10.1016/j.techfore.2016.08.019

    Article  Google Scholar 

  41. Korinek, A., Stiglitz, J.E.: Artificial Intelligence, Globalization, and Strategies for Economic Development. National Bureau of Economic Research, Cambridge (2021)

    Book  Google Scholar 

  42. Yayboke, E., Carter, W.A.: The need for a leapfrog strategy. In: Cent. Strateg. Int. Stud. https://www.csis.org/analysis/need-leapfrog-strategy. (2020). Accessed 25 Feb 2022.

  43. Dafoe, A.: AI governance: a research agenda. Gov AI Program Future Humanity Inst University Oxf Oxf 1442:1443 (2018)

  44. OECD.: AI Policy Observatory Policy initiatives for China. In: OECD AI Policy Obs. https://oecd.ai/en/dashboards/policy-initiatives?conceptUris=http:%2F%2Fkim.oecd.org%2FTaxonomy%2FGeographicalAreas%23China. Accessed 28 Feb 2022

  45. Roberts, H., Cowls, J., Morley, J., et al.: The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. AI Soc. 36, 59–77 (2021). https://doi.org/10.1007/s00146-020-00992-2

    Article  Google Scholar 

  46. Sheehan, M.: China’s New AI Governance Initiatives Shouldn’t Be Ignored. In: Carnegie Endow. Int. Peace. https://carnegieendowment.org/2022/01/04/china-s-new-ai-governance-initiatives-shouldn-t-be-ignored-pub-86127 (2022). Accessed 24 Jan 2022.

  47. European Commission.: A European approach to artificial intelligence | Shaping Europe’s digital future. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence. Accessed 1 Mar 2022.

  48. OECD.: AI Policy Observatory Policy initiatives of the EU. In: OECD AI Policy Obs. https://oecd.ai/en/dashboards/policy-initiatives?conceptUris=http:%2F%2Fkim.oecd.org%2FTaxonomy%2FOrganisations%23EuropeanUnion. Accessed 1 Mar 2022.

  49. National Science and Technology Council.: The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update. National Science and Technology Council (2019).

  50. National Security Commission on Artificial Intelligence.: The National Security on Artificial Intelligence. National Security Commission on Artificial Intelligence (2021)

  51. OECD.: AI Policy Observatory Policy initiatives of United States. In: OECD AI Policy Obs. https://oecd.ai/en/dashboards/policy-initiatives?conceptUris=http:%2F%2Fkim.oecd.org%2FTaxonomy%2FGeographicalAreas%23UnitedStates. Accessed 1 Mar 2022.

  52. Office of Science and Technology Policy.: American Artificial Intelligence Initiative: Year One Annual Report. The White House (2020).

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Authors and Affiliations

  1. Hubert H. Humphrey School of Public Affairs, Minneapolis, MN, USA

    Ren Bin Lee Dixon

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Appendices

Appendix

See Tables 3, 4, 5 and 6.

Table 3 Overview of AI principles developed worldwide
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Table 4 Overview of key governance frameworks from China
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Table 5 Overview of key governance frameworks from the European Union
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Table 6 Overview of key governance frameworks from the United States
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Research Notes

Summarized definition of AI principles.

Matrix table of AI Principles with the Ethical Norms (China), the AI Act (European Union), and the Guidance (United States).

AI Principles: A Comparative Analysis of the Ethical Norms (China), the AI Act (European Union), and the Guidance (United States).

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Dixon, R.B.L. A principled governance for emerging AI regimes: lessons from China, the European Union, and the United States. AI Ethics 3, 793–810 (2023). https://doi.org/10.1007/s43681-022-00205-0

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