Addressing Fairness and Explainability in Image Classification Using Optimal Transport

Our new paper, with François Hu and Philipp Ratz, Addressing Fairness and Explainability in Image Classification Using Optimal Transport, is now available on ArXiv.

Algorithmic Fairness and the explainability of potentially unfair outcomes are crucial for establishing trust and accountability of Artificial Intelligence systems in domains such as healthcare and policing. Though significant advances have been made in each of the fields separately, achieving explainability in fairness applications remains challenging, particularly so in domains where deep neural networks are used. At the same time, ethical data-mining has become ever more relevant, as it has been shown countless times that fairness-unaware algorithms result in biased outcomes. Current approaches focus on mitigating biases in the outcomes of the model, but few attempts have been made to try to explain why a model is biased. To bridge this gap, we propose a comprehensive approach that leverages optimal transport theory to uncover the causes and implications of biased regions in images, which easily extends to tabular data as well. Through the use of Wasserstein barycenters, we obtain scores that are independent of a sensitive variable but keep their marginal orderings. This step ensures predictive accuracy but also helps us to recover the regions most associated with the generation of the biases. Our findings hold significant implications for the development of trustworthy and unbiased AI systems, fostering transparency, accountability, and fairness in critical decision-making scenarios across diverse domains.



Cite this blog post
Arthur Charpentier (2023, August 21). Addressing Fairness and Explainability in Image Classification Using Optimal Transport. Freakonometrics. Retrieved June 24, 2024, from https://doi.org/10.58079/ovmk

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