Ethics, society, humanity, & AI

Computational technologies such as data science, machine learning, and robotics are reshaping almost every aspect of society, and so it is critical that we understand the ways that they do—and more frequently do not—advance our values and interests. My research in this space aims to proactively shape the design, development, and deployment of these technologies through collaborations, rather than being merely critical and reactive. I aim to make a real-world impact by promoting and enabling more ethical data science, machine learning, and AI.

One aspect of this research has explored cross-cutting issues that arise across all sectors, particularly the relation of trust (both human-machine and human-human) and challenges of algorithmic bias. A different aspect of my research has explored issues that are distinctive to particular sectors, including transportation; healthcare; surveillance; and defense & security. This latter work includes examination of ethical issues around disinformation and synthetic media (e.g., deepfakes).

Selected publications:

• Fazelpour, S., Lipton, Z. C., & Danks, D. (in press). Algorithmic fairness and the situated dynamics of justice. Canadian Journal of Philosophy.

• Danks, D. (2022). Governance via explainability. In J. Bullock (Ed.), The Oxford handbook of AI governance. Oxford: Oxford University Press.

• Danks, D. (2021). Digital ethics as translational ethics. In I. Vasiliu-Feltes & J. Thomason (Eds.), Applied ethics in a digital world (pp. 1-15). IGI Global.

• Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8), e12760.

• Golden, P., & Danks, D. (2021). Ethical obligations to provide novelty. In Proceedings of the 2021 AAAI/ACM Conference on Artificial Intelligence, Ethics, & Society.

• Montague, E., Day, T. E., Barry, D., Brumm, M., McAdie, A., Cooper, A. B., Wignall, J., Erdman, S., Núñez, D., Diekema, D., & Danks, D. (2021). The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children’s Hospital. Journal of the American Medical Informatics Association, 28, 650-652.

• Lu, J., Lee, D., Kim, T. W., & Danks, D. (2020). Good explanation for algorithmic transparency. In Proceedings of the 2020 AAAI/ACM Conference on Artificial Intelligence, Ethics, & Society.

• Zhou, Y., & Danks, D. (2020). Different “intelligibility” for different folks. In Proceedings of the 2020 AAAI/ACM Conference on Artificial Intelligence, Ethics, & Society.

• Geary, T., & Danks, D. (2019). Balancing the benefits of autonomous vehicles. In Proceedings of the 2019 AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society.

• LaRosa, E., & Danks, D. (2018). Impacts on trust of healthcare AI. In Proceedings of the 2018 AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society.

• London, A. J., & Danks, D. (2018). Regulating autonomous vehicles: A policy proposal. In Proceedings of the 2018 AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society.

• Roff, H. M., & Danks, D. (2018). "Trust but Verify": The difficulty of trusting autonomous weapons systems. Journal of Military Ethics, 17, 2-20.

• Danks, D., & London, A. J. (2017). Algorithmic bias in autonomous systems. In C. Sierra (Ed.), Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 4691-4697).

• Danks, D., & London, A. J. (2017). Regulating autonomous systems: Beyond standards. IEEE Intelligent Systems, 32, 88-91.

• Danks, D., & Danks, J. H. (2016). Beyond machines: Humans in cyber operations, espionage, and conflict. In F. Allhoff, A. Henschke, & B. J. Strawser (Eds.), Binary bullets: The ethics of cyberwarfare (pp. 177-197). Oxford: Oxford University Press.

• Danks, D. (2014). A modern Pascal's wager for mass electronic surveillance. Telos, 169, 155-161.

• Danks, D., & Danks, J. H. (2013). The moral permissibility of automated responses during cyberwarfare. Journal of Military Ethics, 12, 18-33.