There is now widespread recognition that advances in AI and related technologies have deep ethical and societal implications. At the same time, there is much less consensus about what we should expect from AI ethics. In this talk, I will first argue that ethical analyses cannot be treated as a secondary or optional aspect of technology creation. No AIs are outside of the scope of ethics, though the ethical content of an AI is often different than people think. I will then argue that AI ethics should be a translational ethics: a robust, multi-disciplinary effort that starts with the practices of AI design, development, and deployment, and then develops practical guidance to produce more ethical AI. Throughout the talk, I will provide concrete examples of AI ethics as translational ethics.
David Danks is Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California, San Diego. His research interests are at the intersection of philosophy, cognitive science, and machine learning, using ideas, methods, and frameworks from each to advance our understanding of complex, interdisciplinary problems. Danks has examined the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He has also done research on computational cognitive science and causal discovery algorithms. Danks is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship.