1 citations · 1 across the 8 of their papers we have counts for
9 papers
Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale
Alessandro Morosini, Sarah H. Cen, Andrew Ilyas +3
Personalization algorithms determine what content users encounter on online platforms. Auditing these systems is difficult because independent auditors have only black-box access t…
Conformal Language Modeling via Posterior Sampling
Nicolas Emmenegger, Theo X. Olausson, Armando Solar-Lezama +1
Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using statistical techniques based on conformal prediction, with both theore…
Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research
Nicolas Emmenegger, Ellery Stahler, Chara Podimata
Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. In AI evaluation, these tasks m…
Do LLMs Track Public Opinion? A Multi-Model Study of Favorability Predictions in the 2024 U.S. Presidential Election
Riya Parikh, Sarah H. Cen, Chara Podimata
We investigate whether Large Language Models (LLMs) can track public opinion as measured by exit polls during the 2024 U.S. presidential election cycle. Our analysis focuses on hea…
Contextual Dynamic Pricing with Heterogeneous Buyers
Thodoris Lykouris, Sloan Nietert, Princewill Okoroafor +2
We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over rounds) that depend on the observabl…
Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
Valia Efthymiou, Ekaterina Fedorova, Chara Podimata
Strategic classification examines how decision rules interact with agents who strategically adapt their features. Most existing models focus on maximizing predictive performance, a…
Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season
Sarah H. Cen, Andrew Ilyas, Hedi Driss +4
The 2024 US presidential election is the first major contest to occur in the US since the popularization of large language models (LLMs). Building on lessons from earlier shifts in…
Can Users Fix Algorithms? A Game-Theoretic Analysis of Collective Content Amplification in Recommender Systems
Ekaterina Fedorova, Madeline Kitch, Chara Podimata
Users of social media platforms based on recommendation systems (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some…
Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality
Chara Podimata
The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modi…