1 citations · 1 across the 5 of their papers we have counts for
5 papers
Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
Ainhize Barrainkua, Santiago Mazuelas, Novi Quadrianto +1
As automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases. In the light of thi…
Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano +1
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring…
Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage insta…
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the…
A Survey on Preserving Fairness Guarantees in Changing Environments
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The lit…