How Do We Distribute Responsibility When AI Goes Wrong?

One company builds the model. Another tweaks the model. Who’s responsible when things go sideways? David Danks is a Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California, San Diego. His research interests range widely across philosophy, cognitive science, and machine learning, including their intersection. Danks 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.

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I talk with the smartest people I can find working or researching anywhere near the intersection of emerging technologies and their ethical impacts. From AI to social media to quantum computers and blockchain. From hallucinating chatbots to AI judges to who gets control over decentralized applications. If it’s coming down the tech pipeline (or it’s here already), we’ll pick it apart, figure out its implications, and break down what we should do about it.