Skip to main content
SpringerLink
Log in

Enhancing Ethical Governance of Artificial Intelligence Through Dynamic Feedback Mechanism

  • Conference paper
  • First Online:
Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((volume 14598))

Included in the following conference series:

  • 48 Accesses

Abstract

The rapid advancement of Artificial Intelligence (AI) has ushered in significant opportunities while also giving rise to profound ethical concerns. Governments, non-governmental organizations, research institutions, and industries worldwide have fervently engaged in the exploration and implementation of AI ethics. This work reviews the research and practice of AI ethical governance issues worldwide, with a particular emphasis on AI ethics legislation and the practical application of ethical principles. Within the current landscape of AI ethics governance research, several pressing challenges emerge, including the establishment of a robust ethical decision-making framework, the integration of ethical principles into AI systems, and the formulation of guiding legal policies. Based on an extensive analysis of existing AI ethics governance theories and technological research, this study presents an innovative conceptual framework rooted in dynamic feedback reinforcement learning theory. This pioneering framework, cultivated through the collaboration of multiple stakeholders, encompasses various dimensions encompassing law, technology, and market dynamics. Additionally, it establishes an AI ethics governance committee tasked with supervising the behavior of AI systems, guiding their acquisition of ethical principles, and adapting to the ever-evolving environment. The overarching objective of this collaborative, multi-faceted AI ethics governance framework is to serve as a reference point for global AI ethics governance mechanisms and to promote the sustainable development of the AI industry. By taking into account legal, technological, and market factors, our aim is to facilitate a harmonious interaction between technology, humanity, and society, ultimately paving the way for a healthy and inclusive intelligent society.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. IBM policy. https://www.ibm.com/blogs/policy/trust-principles/. Accessed 16 Sept 2023

  2. Texts adopted - civil law rules on robotics. https://www.europarl.europa.eu/doceo/document/TA-8-2017-0051_EN.html?redirect. Accessed 4 Jan 2024

  3. H.R.3388 - safely ensuring lives future deployment and research in vehicle evolution act, September 2017. https://www.congress.gov/bill/115th-congress/house-bill/3388. Accessed 4 Jan 2024

  4. S.1885 - AV START act, November 2017. https://www.congress.gov/bill/115th-congress/senate-bill/1885. Accessed 4 Jan 2024

  5. AI in the UK: ready, willing and able? (2018). https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/10004.htm. Accessed 16 Sept 2023

  6. The Future Computed: Artificial Intelligence and Its Role in Society. Independently Published, Washington (2018)

    Google Scholar 

  7. AI principles: Recommendations on the ethical use of artificial intelligence by the department of defense (2020). https://admin.govexec.com/media/dib_ai_principles_-_supporting_document_-_embargoed_copy_(oct_2019).pdf. Accessed 16 Sept 2023

  8. Singapore launches world’s first AI testing framework and toolkit to promote transparency; invites companies to pilot and contribute to international standards development, May 2022. https://www.imda.gov.sg/resources/press-releases-factsheets-and-speeches/press-releases/2022/sg-launches-worlds-first-ai-testing-framework-and-toolkit-to-promote-transparency. Accessed 4 Jan 2024

  9. Abrams, M., Abrams, J., Cullen, P., Goldstein, L.: Artificial intelligence, ethics, and enhanced data stewardship. IEEE Secur. Priv. 17(2), 17–30 (2019)

    Article  Google Scholar 

  10. Armstrong, S.: Motivated value selection for artificial. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  11. Ashok, M., Madan, R., Joha, A., Sivarajah, U.: Ethical framework for artificial intelligence and digital technologies. Int. J. Inf. Manage. 62(102433), 102433 (2022)

    Article  Google Scholar 

  12. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)

    Article  Google Scholar 

  13. Balakrishnan, A., Bouneffouf, D., Mattei, N., Rossi, F.: Incorporating behavioral constraints in online AI systems. Proc. Conf. AAAI Artif. Intell. 33(01), 3–11 (2019)

    Google Scholar 

  14. Briggs, G.M., Scheutz, M.: Sorry, I can’t do that: developing mechanisms to appropriately reject directives in human-robot. In: AAAI Fall Symposia, pp. 32–36 (2015)

    Google Scholar 

  15. Calmon, F.P., Wei, D., Ramamurthy, K.N., Varshney, K.R.: Optimized data pre-processing for discrimination prevention (2017)

    Google Scholar 

  16. European Commission: White paper on artificial intelligence: a European approach to excellence and trust, February 2020. https://ec.europa.eu/futurium/en/system/files/ged/white_paper_ai_19_02_2020.pdf. Accessed 5 Jan 2024

  17. David, A., James, M., Michael, L.L.: Reinforcement learning as a framework for ethical decision making. In: The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence Providence, pp. 54–61 (2016)

    Google Scholar 

  18. Edwards, L., Veale, M.: Slave to the algorithm: why a right to an explanation is probably not the remedy you are looking for. Duke Law Technol. Rev. (18) (2017)

    Google Scholar 

  19. ElShawi, R., Sherif, Y., Al-Mallah, M., Sakr, S.: ILIME: local and global interpretable model-agnostic explainer of black-box decision. In: Welzer, T., Eder, J., Podgorelec, V., Kamišalić Latifić, A. (eds.) ADBIS 2019. LNCS, vol. 11695, pp. 53–68. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28730-6_4

    Chapter  Google Scholar 

  20. Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 329–338 (2019)

    Google Scholar 

  21. Future of Life: AI principles, August 2017. https://futureoflife.org/ai-principles/. Accessed 16 Sept 2023

  22. Haitao, G.: How did Chinese internet enterprises participate in internet governance-a case study based on Baidu, Alibaba and Tencent (bat). J. Commun. 28(53–69+127) (2021)

    Google Scholar 

  23. Hajian, S.: Simultaneous discrimination prevention and privacy protection in data publishing and mining (2013)

    Google Scholar 

  24. High-Level Expert Group on Artificial Intelligence: Ethics guidelines for trustworthy AI. European Commission (2019)

    Google Scholar 

  25. Huang, L., Yan, H.: CICC: the EU is concerned about the impact on the development path of AI through legislative initiatives, November 2020. https://www.sohu.com/a/431693843_463913. Accessed 4 Jan 2024

  26. IEEE: Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, November 2017. Accessed 16 Sept 2023

    Google Scholar 

  27. Ienca, M., et al.: Towards a governance framework for brain data. Neuroethics 15(2) (2022)

    Google Scholar 

  28. Tencent AI Lab: Technology ethics in the intelligent era - reshaping trust in the digital society, July 2019. https://www.tisi.org/10890. Accessed 16 Sept 2023

  29. Liu, X., Xiao, H.: Ethical governance of artificial intelligence in the soft law paradigm: an analysis of the Japanese institution. Contemp. Econ. Japan 04(28–44) (2023). https://doi.org/10.16123/j.cnki.issn.1000-355x.2023.04.003

  30. OECD: AI-principles overview, May 2019. https://www.oecd.ai/ai-principles. Accessed 16 Sept 2023

  31. Peets, L., Hansen, M., Choi, S.J., Drake, M.: The council of Europe publishes feasibility study on developing a legal instrument for ethical AI, January 2021. https://www.insideprivacy.com/artificial-intelligence/the-council-of-europe-publishes-feasibility-study-on-developing-a-legal-instrument-for-ethical-ai/. Accessed 4 Jan 2024

  32. Peters, D., Vold, K., Robinson, D., Calvo, R.A.: Responsible AI–two frameworks for ethical design practice. IEEE Trans. Technol. Soc. 1(1), 34–47 (2020)

    Article  Google Scholar 

  33. Pichai, S.: AI at Google: our principles, June 2018. https://www.blog.google/technology/ai/ai-principles/. Accessed 16 Sept 2023

  34. Rahwan, I.: Society-in-the-loop: programming the algorithmic social contract. Ethics Inf. Technol. 20(1), 5–14 (2018)

    Article  Google Scholar 

  35. Reddy, S., Allan, S., Coghlan, S., Cooper, P.: A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 27(3), 491–497 (2020)

    Article  Google Scholar 

  36. Ribeiro, M., Singh, S., Guestrin, C.: “why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. Association for Computational Linguistics, Stroudsburg, PA, USA (2016)

    Google Scholar 

  37. Riedl, M.: Big red button by Mark Riedl (2016)

    Google Scholar 

  38. Robbins, S.: AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines. AI Soc. 35(2), 391–400 (2020)

    Article  Google Scholar 

  39. Shankaranarayana, S.M., Runje, D.: ALIME: autoencoder based approach for local interpretability. ArXiv, abs (1909)

    Google Scholar 

  40. Shen, Z.: On California consumer privacy law, July 2019. http://rmfyb.chinacourt.org/paper/html/2019-07/12/content_157608.htm?div=-1. Accessed 4 Jan 2024

  41. Shimizu, R., Matsutani, M., Goto, M.: An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information. Knowl. Based Syst. 239(107970), 107970 (2022)

    Article  Google Scholar 

  42. Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences (2017)

    Google Scholar 

  43. Tencent Research Institute: Ten recommendations for interpreting new trends in EU AI legislation, November 2020. https://baijiahao.baidu.com/s?id=1559578568643940 &wfr=spider &for=pc. Accessed 4 Jan 2024

  44. Thomas, A., Scheutz, M.: The “big red button” is too late: an alternative model for the ethical evaluation of AI systems. Ethics Inf. Technol. 20(1), 59–69 (2018)

    Google Scholar 

  45. Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA (2019)

    Google Scholar 

  46. Zhou, Z., Hooker, G., Wang, F.: S-LIME: stabilized-LIME for model explanation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA (2021)

    Google Scholar 

Download references

Acknowledgement

This paper is supported by “the Fundamental Research Funds for the Central Universities”, Zhongnan University of Economics and Law (202451406).

Author information

Authors and Affiliations

  1. Zhongnan University of Economics and Law, Wuhan, China

    Yaqi Liu, Wenjie Zheng & Yueli Su

Authors
  1. Yaqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjie Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yueli Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaqi Liu .

Editor information

Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

Appendix Overview of Major AI Ethical Guidelines

Appendix Overview of Major AI Ethical Guidelines

See Tables 1, 2, 3, 4 and 5.

Table 1. Ethical Guidelines for Representative Artificial Intelligence in International Organizations
Full size table
Table 2. Ethical Guidelines for Representative Artificial Intelligence in Government Organizations
Full size table
Table 3. The representative ethical principles of artificial intelligence in colleges and universities
Full size table
Table 4. The representative ethical principles of artificial intelligence in private enterprises
Full size table
Table 5. The representative ethical principles of artificial intelligence in industry association
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y., Zheng, W., Su, Y. (2024). Enhancing Ethical Governance of Artificial Intelligence Through Dynamic Feedback Mechanism. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14598. Springer, Cham. https://doi.org/10.1007/978-3-031-57867-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57867-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57866-3

  • Online ISBN: 978-3-031-57867-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation