2 citations · 4 across the 6 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…
Bias in the Loop: How Humans Evaluate AI-Generated Suggestions
Jacob Beck, Stephanie Eckman, Christoph Kern +1
Human-AI collaboration increasingly drives decision-making across industries, from medical diagnosis to content moderation. While AI systems promise efficiency gains by providing a…
Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication
Stephanie Eckman, Bolei Ma, Christoph Kern +3
Models trained on crowdsourced annotations may not reflect population views, if those who work as annotators do not represent the broader population. In this paper, we propose PAIR…