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Michigan Institute for Data Science pres.

Fair Ranking with Biased Data

Thorsten Joachims

https://umich.zoom.us/j/93790126046 https://umich.zoom.us/j/93790126046
https://umich.zoom.us/j/93790126046
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 immense power in shaping markets and allocating opportunity to the participants. In this talk, I will discuss how the machine learning algorithms underlying these systems 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.
https://umich.zoom.us/j/93790126046 https://umich.zoom.us/j/93790126046
https://umich.zoom.us/j/93790126046
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