11 citations · 13 across the 6 of their papers we have counts for
6 papers
Accurate in space, unreliable in time: how LLMs represent national cultural change
Yalda Daryani, Miranda Bogen, Madeleine I. G. Daepp
Assessments of cultural alignment have become an important part of the development and improvement of large language models (LLMs). However, the majority of the evaluations treat c…
The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
Emily Black, Miranda Bogen, Logan Koepke +3
U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about ho…
Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains
Emaan Bilal Khan, Amy Winecoff, Miranda Bogen +1
Foundation models are routinely fine-tuned for use in particular domains, yet safety assessments are typically conducted only on base models, implicitly assuming that safety proper…
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
Improving governance outcomes through AI documentation: Bridging theory and practice
Amy A. Winecoff, Miranda Bogen
Documentation plays a crucial role in both external accountability and internal governance of AI systems. Although there are many proposals for documenting AI data, models, systems…
On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman +22
Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defin…