6 citations · 35 across the 26 of their papers we have counts for
6 papers · 1 filter
Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether t…
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
Sarah Dean, Evan Dong, Meena Jagadeesan +1
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of…
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
Kate Donahue, Nicole Immorlica, Meena Jagadeesan +2
When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many su…
Feedback Loops With Language Models Drive In-Context Reward Hacking
Alexander Pan, Erik Jones, Meena Jagadeesan +1
Language models influence the external world: they query APIs that read and write to web pages, generate content that shapes human behavior, and run system commands as autonomous a…
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab +2
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalign…
Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
Nicole Immorlica, Meena Jagadeesan, Brendan Lucier
Online content platforms commonly use engagement-based optimization when making recommendations. This encourages content creators to invest in quality, but also rewards gaming tric…