11 citations · 15 across the 5 of their papers we have counts for
3 papers · 1 filter
Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
Jan Simson, Alessandro Fabris, Cosima Fröhner +2
As machine learning (ML) systems are increasingly adopted in high-stakes decision-making domains, ensuring fairness in their outputs has become a central challenge. At the core of…
Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond
Marina Ceccon, Giandomenico Cornacchia, Davide Dalle Pezze +2
Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legis…
Lazy Data Practices Harm Fairness Research
Jan Simson, Alessandro Fabris, Christoph Kern
Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancemen…