6 citations · 35 across the 26 of their papers we have counts for
30 papers
LLM Detection as an Intervention: Downstream Impact under Strategic User Behavior
Meena Jagadeesan, Tatsunori Hashimoto, Jon Kleinberg
As LLM adoption becomes more widespread, there is a growing interest in detecting LLM-generated content, for example through LLM detection tools and through heuristics based on lan…
Strategic Feature Selection
Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6
When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The ty…
Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability
Alyssa Unell, Natalie Dullerud, Naomi Boneh +4
LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation. However, the reliability of these judges depends critically on their alignme…
Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
Alyssa Unell, Miguel Fuentes, Brenna Li +4
Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks…
Power and Limitations of Aggregation in Compound AI Systems
Nivasini Ananthakrishnan, Meena Jagadeesan
When designing compound AI systems, a common approach is to query multiple copies of the same model and aggregate the responses to produce a synthesized output. Given the homogenei…
Breaking Algorithmic Collusion in Human-AI Ecosystems
Natalie Collina, Eshwar Ram Arunachaleswaran, Meena Jagadeesan
AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems fro…
Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?
S. Nageeb Ali, Nicole Immorlica, Meena Jagadeesan +1
In the digital economy, technological innovations make it cheaper to produce high-quality content. For example, generative AI tools reduce costs for creators who develop content to…
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…
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt +1
As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the…
Incentivizing High-Quality Content in Online Recommender Systems
Xinyan Hu, Meena Jagadeesan, Michael I. Jordan +1
In content recommender systems such as TikTok and YouTube, the platform's recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which…
Competition, Alignment, and Equilibria in Digital Marketplaces
Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in da…
Supply-Side Equilibria in Recommender Systems
Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt
Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also producer incentives. Producers seek to create content that will be shown by t…
Performative Power
Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner
We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to cause cha…
Regret Minimization with Performative Feedback
Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner
In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs t…
Learning Equilibria in Matching Markets from Bandit Feedback
Meena Jagadeesan, Alexander Wei, Yixin Wang +2
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of…
Alternative Microfoundations for Strategic Classification
Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt
When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory un…
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar
We provide a function space characterization of the inductive bias resulting from minimizing the norm of the weights in multi-channel convolutional neural networks with li…
Individual Fairness in Pipelines
Cynthia Dwork, Christina Ilvento, Meena Jagadeesan
It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline com…
Individual Fairness in Advertising Auctions through Inverse Proportionality
Shuchi Chawla, Meena Jagadeesan
Recent empirical work demonstrates that online advertisement can exhibit bias in the delivery of ads across users even when all advertisers bid in a non-discriminatory manner. We s…