1.1k citations · 1.2k across the 20 of their papers we have counts for
22 papers
Reconciling the accuracy-diversity trade-off in recommendations
Kenny Peng, Manish Raghavan, Emma Pierson +2
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items repres…
Interface Design to Mitigate Inflation in Recommender Systems
Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov +1
Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations. An implicit assumption when aggregating ratings into item qu…
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
Smitha Milli, Emma Pierson, Nikhil Garg
Many recommender systems optimize a linear weighting of different user behaviors, such as clicks, likes, and shares. We analyze the optimal choice of weights from the perspectives…
Coarse race data conceals disparities in clinical risk score performance
Rajiv Movva, Divya Shanmugam, Kaihua Hou +4
Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown,…
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…
Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing
Zhi Liu, Uma Bhandaram, Nikhil Garg
Modern city governance relies heavily on crowdsourcing to identify problems such as downed trees and power lines. A major concern is that residents do not report problems at the sa…
Fair ranking: a critical review, challenges, and future directions
Gourab K Patro, Lorenzo Porcaro, Laura Mitchell +3
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and…
Strategic Ranking
Lydia T. Liu, Nikhil Garg, Christian Borgs
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the eff…
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps
Zhi Liu, Nikhil Garg
Due to the Covid-19 pandemic, more than 500 US-based colleges and universities went "test-optional" for admissions and promised that they would not penalize applicants for not subm…
Combatting Gerrymandering with Ranked Choice Voting: An Experimental Analysis of Multi-member Districts in the United States
Nikhil Garg, Wes Gurnee, David Rothschild +1
Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States -- Winner takes all single-m…
Dropping Standardized Testing for Admissions Trades Off Information and Access
Nikhil Garg, Hannah Li, Faidra Monachou
We study the role of information and access in capacity-constrained selection problems with fairness concerns. We develop a statistical discrimination framework, where each applica…
Fair Allocation through Selective Information Acquisition
William Cai, Johann Gaebler, Nikhil Garg +1
Public and private institutions must often allocate scare resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelih…
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates
Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong +1
Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters' preferences. In practice,…
Driver Surge Pricing
Nikhil Garg, Hamid Nazerzadeh
Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side pay…
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Dorottya Demszky, Nikhil Garg, Rob Voigt +4
We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify thes…
Designing Informative Rating Systems: Evidence from an Online Labor Market
Nikhil Garg, Ramesh Johari
Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, these ratings are often highly inflated, and therefore not very infor…
Markets for Public Decision-making
Nikhil Garg, Ashish Goel, Benjamin Plaut
A public decision-making problem consists of a set of issues, each with multiple possible alternatives, and a set of competing agents, each with a preferred alternative for each is…
Designing Optimal Binary Rating Systems
Nikhil Garg, Ramesh Johari
Modern online platforms rely on effective rating systems to learn about items. We consider the optimal design of rating systems that collect binary feedback after transactions. We…
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky +1
Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words. In this paper, we develop a framework to dem…
Iterative Local Voting for Collective Decision-making in Continuous Spaces
Nikhil Garg, Vijay Kamble, Ashish Goel +2
Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These…
Impact of Dual Slope Path Loss on User Association in HetNets
Nikhil Garg, Sarabjot Singh, Jeffrey Andrews
Intelligent load balancing is essential to fully realize the benefits of dense heterogeneous networks. Current techniques have largely been studied with single slope path loss mode…