26 citations · 41 across the 5 of their papers we have counts for
9 papers
Comparing Human and Machine Bias in Face Recognition
Samuel Dooley, Ryan Downing, George Wei +10
Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceiv…
Robustness Disparities in Commercial Face Detection
Samuel Dooley, Tom Goldstein, John P. Dickerson
Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Critiques that focus on system performance…
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri, Michael J. Curry, Samuel Dooley +1
The design of optimal auctions is a problem of interest in economics, game theory and computer science. Despite decades of effort, strategyproof, revenue-maximizing auction designs…
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Kevin Kuo, Anthony Ostuni, Elizabeth Horishny +5
The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectru…
The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers
Samuel Dooley, John P. Dickerson
In many labor markets, workers and firms are connected via affiliative relationships. A management consulting firm wishes to both accept the best new workers but also place its cur…
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Vedant Nanda, Samuel Dooley, Sahil Singla +2
Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these…