When Biased AI is Good

Everyone knows biased or discriminatory AI bad and we need to get rid of it, right? Well, not so fast.

I talk to David Danks, a professor of data science and philosophy at UCSD. He and his research team argue that we need to reconceive how we think about biased AI. In some cases, David thinks, they can be beneficial. Good policy – both corporate and regulatory – needs to take this into account.

It was a great discussion and seemed like the perfect way to kick off Ethical Machines. I hope you enjoy it. More importantly, I hope you get something out of it.

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.