Year: 2024
Type: conference-paper
Abstract: As reliance on Machine Learning (ML) systems in real-world decision-making processes grows, ensuring these systems are free of bias against sensitive demographic groups is of increasing importance. Existing techniques for automatically debiasing ML models generally require access to either the models’ internal architectures, the models’ training datasets, or both. In this paper we outline the reasons why such requirements are disadvantageous, and present an alternative novel debiasing system that is both data- and model-agnostic. We implement this system as a Reinforcement Learning Agent and through extensive experiments show that we can debias a variety of target ML model architectures over three benchmark datasets. Our results show performance comparable to data- and/or model-gnostic state-of-the-art debiasers.
Authors: Thomas Pinkava, Jack McFarland, Afra Mashhadi
Institution: University of Washington
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W4404518634