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Domain Adaptation meets Individual Fairness. And they get along.
Debarghya Mukherjee · Felix Petersen · Mikhail Yurochkin · Yuekai Sun

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #715

Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we leverage this connection between algorithmic fairness and distribution shifts to show that algorithmic fairness interventions can help ML models overcome distribution shifts, and that domain adaptation methods (for overcoming distribution shifts) can mitigate algorithmic biases. In particular, we show that (i) enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models under the covariate shift assumption and that (ii) it is possible to adapt representation alignment methods for domain adaptation to enforce individual fairness. The former is unexpected because IF interventions were not developed with distribution shifts in mind. The latter is also unexpected because representation alignment is not a common approach in the individual fairness literature.

Author Information

Debarghya Mukherjee (University of Michigan)
Felix Petersen (Stanford University)
Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab)

I am a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. My research interests are - Algorithmic Fairness - Out-of-Distribution Generalization - Applications of Optimal Transport in Machine Learning - Model Fusion and Federated Learning Before joining IBM, I completed my PhD in Statistics at the University of Michigan, where I worked with Long Nguyen. I received my Bachelor's degree in applied mathematics and physics from Moscow Institute of Physics and Technology.

Yuekai Sun (University of Michigan)

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