Approximate Inference for the Bayesian Fairness Framework
Paper i proceeding, 2023

As the impact of Artificial Intelligence systems and applications on everyday life increases, algorithmic fairness undoubtedly constitutes one of the major problems in our modern society. In the current paper, we extend the work of Dimitrakakis et al. on Bayesian fairness [1] that incorporates models uncertainty to achieve fairness, proposing a practical algorithm with the aim to scale the framework for a broader range of applications. We begin by applying the bootstrap technique as a scalable alternative to approximate the posterior distribution of parameters of the fully Bayesian viewpoint. To make the Bayesian fairness framework applicable to more general data settings, we define an empirical formulation suitable for the continuous case. We experimentally demonstrate the potential of the framework from an extensive evaluation study on a real dataset and different decision settings.

Machine Learning

Decision Making

Algorithmic Fairness

Bayesian Fairness


Andreas Athanasopoulos

Université de Neuchâtel

Amanda Belfrage

Student vid Chalmers

David Berg Marklund

Student vid Chalmers

Christos Dimitrakakis

Université de Neuchâtel

Chalmers, Data- och informationsteknik, Data Science och AI

Universitetet i Oslo

CEUR Workshop Proceedings

16130073 (ISSN)

Vol. 3442

2nd European Workshop on Algorithmic Fairness, EWAF 2023
Winterthur, Switzerland,


Sannolikhetsteori och statistik

Datavetenskap (datalogi)

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