no code implementations • 22 Apr 2024 • Hilde Weerts, Aislinn Kelly-Lyth, Reuben Binns, Jeremias Adams-Prassl
In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic.
no code implementations • 18 Apr 2024 • Hilde Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy
While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to 'algorithmic positive action' under European Union (EU) non-discrimination law.
no code implementations • 5 May 2023 • Hilde Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy
In this paper, we aim to illustrate to what extent European Union (EU) non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature and where they differ.
no code implementations • 29 Mar 2023 • Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio
Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.
no code implementations • 15 Mar 2023 • Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.
no code implementations • 17 Feb 2022 • Hilde Weerts, Lambèr Royakkers, Mykola Pechenizkiy
In this paper, we present a framework for moral reasoning about the justification of fairness metrics and explore the moral implications of the use of fair-ml algorithms that optimize for them.