4 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Mitigating Label Bias with Interpretable Rubric Embeddings
Calvin Isley, Johann D. Gaebler, Sharad Goel
Statistical decision algorithms are increasingly deployed in domains where ground-truth labels are hard to obtain, such as hiring, university admissions, and content moderation. In…
stat.AP2024
A Simple, Statistically Robust Test of Discrimination
Johann D. Gaebler, Sharad Goel
In observational studies of discrimination, the most common statistical approaches consider either the rate at which decisions are made (benchmark tests) or the success rate of tho…
stat.AP2024★ 4 cited
Auditing the Use of Language Models to Guide Hiring Decisions
Johann D. Gaebler, Sharad Goel, Aziz Huq +1
Regulatory efforts to protect against algorithmic bias have taken on increased urgency with rapid advances in large language models (LLMs), which are machine learning models that c…