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In the final episode of our re:Invent series, we're joined by Thorsten Joachims, Professor in the Department of Computer Science at Cornell University.
Thorsten participated at the conference's AI Summit, presenting his research on "Unbiased Learning from Biased User Feedback." In our conversation, we take a look at some of the inherent and introduced biases in recommender systems, and the ways to avoid them. We also discuss how inference techniques can be used to make learning algorithms more robust to bias, and how these can be enabled with the correct type of logging policies.
