Making ML Models fairer through explanations, feature dropout, and aggregation

Abstract: Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that biased decisions may have on individuals or on society as a whole. Not only unfair outcomes affect human rights, they also undermine public trust in ML and AI. In this talk, we will address fairness issues of ML models based on decision outcomes, and we will show how the simple idea of "feature dropout" followed by an "ensemble approach" can improve model fairness without compromising its accuracy. To illustrate we will present a general workflow that relies on explainers to tackle "process fairness", which essentially measures a model's reliance on sensitive or discriminatory features. We will present different applications and empirical settings that show improvements not only with respect to process fairness but also other fairness metrics.

data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability

Audience: researchers in the topic

( video )

Mathematics, Physics and Machine Learning (IST, Lisbon)

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Zoom link: videoconf-colibri.zoom.us/j/91599759679