19 citations · 23 across the 5 of their papers we have counts for
6 papers
From Constitutions to Control: Interpretable Rewards for Aligning Language Models
Johann D. Gaebler, Calvin Isley, Max Lamparth +2
Current approaches to aligning language models often make it hard to know what behavior is being rewarded or to change that reward in a targeted way. In particular, standard prefer…
Receptiveness, Not Sycophancy: Distinguishing Engagement from Deference in Language Models
Calvin Isley, Johann Gaebler, Max Lamparth +2
A central concern with language models is sycophancy: their tendency to defer to users' views at the expense of independent substantive judgment. In parallel, work on social sycoph…
AI-written admissions essays are widespread but penalized
Calvin Isley, Johann D. Gaebler, Sharad Goel
AI is rapidly transforming higher education, including the application process, yet relatively little is known about its use and consequences. To help close this gap, we analyze ne…
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…
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…
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…