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Fair Recommendations with Biased Data

Date: Wednesday, December 1, 2021 16:00 - 18:00
Speaker: Thorsten Joachims (Cornell University)
Location: Zoom: https://istaustria.zoom.us/j/92105664264?pwd=UG1icDk0Ym9GYldZZW41VWlOT0JhUT09 Meeting ID: 921 0566 4264 Passcode: 715403
Series: ELLIS talk
Host: Marco Mondelli
Contact: Ksenja Harpprecht

Search engines and recommender systems have become the dominant matchmaker for a wide range of human endeavors -- from online retail to finding romantic partners. Consequently, they carry substantial power in shaping markets and allocating opportunity to the participants. In this talk, I will discuss how the machine learning algorithms underlying these system can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons often manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and lead to rich-get-richer dynamics. But even when trained with unbiased data, reasons endogenous to the algorithms can lead to unfair or undesirable allocation of opportunity. To overcome these challenges, I will present new machine learning algorithms that directly address both endogenous and exogenous unfairness.


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