Artificial Intelligence (AI) Ethics Research

As artificial intelligence systems increasingly shape decisions in healthcare, hiring, criminal justice, education, and everyday communication, questions about how these systems should to be designed, deployed, and governed has risen to the forefront. AI ethics is a broad and still-evolving field that grapples with issues of bias and fairness, transparency and explainability, accountability when systems fail or cause harm, and the broad societal consequences of automating judgment at scale (such as labor displacement and the spread of misinformation). Because AI systems often operate as opaque "black boxes" whose decisions are difficult to interpret or contest, ethical inquiry in this space is closely intertwined with technical work on explainability, as well as with legal and policy frameworks attempting to assign responsibility and protect those affected. Many key questions remain unsettled even as AI systems become more powerful and more deeply embedded in institutions and daily life.

Some Useful Videos for Learning More About AI Ethics

AI Ethics Scholarly Resources

Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 610–623.

Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62.

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., III, & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.

Hugenholtz, P. B., & Quintais, J. P. (2021). Copyright and artificial creation: Does EU copyright law protect AI-assisted output? International Review of Intellectual Property and Competition Law, 52, 1190–1216.

Hughes, K. D., Konnikov, A., Denier, N., & Hu, Y. (2025). Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review. Human Relations, 79(2), 246–278.

Novelli, C., Taddeo, M., & Floridi, L. (2023). Accountability in artificial intelligence: What it is and how it works. AI & Society, 39, 1871–1882.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in algorithmic healthcare tools. Science, 366(6464), 447–453.

O'Neil, C. (2016). Weapons of math destruction. Crown.

Pasquale, F. (2015). The black box society. Harvard University Press.

Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: Key problems and solutions. AI and Society, 37, 215–230.

Zech, H. (2021). Liability for AI: Public policy considerations. ERA Forum, 22, 147–158.

AI Ethics Research Guides

Miami Dade College

Purdue University

The SALAMANDER project is funded through the generous support of the Institute of Museum and Library Services, Grant Number RE-259543-OLS-26.
This resource guide was created by Dr. Brady Lund and graduate assistant Bryan Anderson. Last Updated September 25, 2026.