The Intersection of Ethics and AI Governance and Survey Results from ML Researchers

AABHINANDAN ASHOK ALMAN

Authors

AABHINANDAN ASHOK ALMAN INDIA Author

Keywords:

Regulatory Frameworks, Transparency, Interdisciplinary Collaboration

Abstract

The rapid advancement of artificial intelligence (AI) technologies has brought forth significant ethical and governance challenges. This paper explores the intersection of ethics and AI governance, examining the frameworks and principles that guide ethical AI development and deployment. Additionally, it presents survey results from machine learning (ML) researchers, shedding light on their perspectives regarding ethical concerns, governance mechanisms, and the future trajectory of AI ethics. The findings reveal a strong consensus on the importance of ethical considerations in AI, highlighting key areas such as transparency, fairness, accountability, and the need for robust regulatory frameworks. The insights from ML researchers underscore the critical role of interdisciplinary collaboration in addressing ethical issues and shaping effective AI governance policies.

References

Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (FAT*). New York, NY, USA: ACM.

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... & Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the 'Good Society': The US, EU, and UK Approach. Science and Engineering Ethics, 24(2), 505-528.

Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1).

Hagendorff, T. (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and Machines, 30(1), 99-120.

Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 1-21.

Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods, and Research to Translate Principles into Practices. Science and Engineering Ethics, 26(4), 2141-2168.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY, USA: Crown Publishing Group.

Whittlestone, J., Nyrup, R., Alexandrova, A., Dihal, K., & Cave, S. (2019). The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES). New York, NY, USA: ACM.