12 citations · 42 across the 15 of their papers we have counts for
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cs.LG2026
Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
David Hartmann, Lena Pohlmann, Lelia Hanslik +3
Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM applications, but suffers from re…
cs.LG2020★ 10 cited
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Pieter Delobelle, Paul Temple, Gilles Perrouin +3
Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming u…