Every day brings new calls for trustworthy AI, or ethical machine learning, or responsible data science, or one of many other goals. However, there is little consensus about exactly how to achieve these goals. In this talk, I will survey the pros & cons of different paths towards “better” AI systems, and argue that translational ethics — research & development to bring ethical insights from the “lab” into practice — provides the most promising way forward. I will give several examples of this kind of translational ethics, connecting them with current AI development practices.

Bio: David Danks is Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California, San Diego. He has examined the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards, including the National AI Advisory Committee.