FairGBM: Gradient Boosting with Fairness Constraints

Priberam Labs

Instituto Superior Técnico, Anfiteatro PA2

Abstract:Tabular data is prevalent in many high-stakes domains, from financial services to public policy. In these settings, Gradient Boosted Machines (GBM) are still the state-of-the-art. However, existing in-training fairness interventions are either incompatible with GBMs, or incur significant performance losses while taking considerably longer to train.We present FairGBM, a framework for training GBMs under fairness constraints, with little to no impact on predictive performance. We validate our method on five large-scale public datasets, as well as a real-world case-study of account opening fraud. Our open-source implementation shows an order of magnitude speedup in training time when compared with related work.https://github.com/feedzai/fairgbmBio:André Cruz holds a Computer Science MSc from FEUP and is currently a PhD student at the Max Planck Institute for Intelligent Systems, in Germany. André&#x27;s current research focus is on Human-ML collaboration and the feedback loops between deployed ML systems and society at large. In the two years prior André worked at Feedzai as part of the FATE AI research group - Fairness, Accountability, Transparency, and Ethics in AI.
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FairGBM: Gradient Boosting with Fairness Constraints

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FairGBM: Gradient Boosting with Fairness Constraints

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