Fair Classification with Adversarial Debiasing

Author(s): Lorenzo Pastore

Originally published on Towards AI.

Photo by Sushil Nash on Unsplash

In this article my colleague Raffaele Anselmo and I analyze a binary classification problem on income prediction in terms of classification and fairness metrics and we propose a fair classifier based on Adversarial Debiasing, along with a Hyperparameters Optimization (HPO).

Git: https://github.com/LorenzoPastore/Adversarial-Fair-Classification

Existing notions of fairness in the machine learning literature are largely inspired by the concept of discrimination in social sciences and law. These notions call for parity (i.e. equality) in treatment, in impact, or both [1]. A decision making process suffers from disparate treatment if its decisions are (partly) based on the subject’s sensitive attribute… Read the full blog for free on Medium.

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Published via Towards AI