no code implementations • 15 Mar 2022 • Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein
This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity.
no code implementations • 1 Mar 2022 • Michael Rawson, Samuel Dooley, Mithun Bharadwaj, Rishabh Choudhary
We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis.
no code implementations • 22 Feb 2022 • Marina Knittel, Samuel Dooley, John P. Dickerson
We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches.
no code implementations • 25 Jan 2022 • Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson
When we compare the size of these disparities to that of commercial models, we conclude that commercial models - in contrast to their relatively larger development budget and industry-level fairness commitments - are always as biased or more biased than an academic model.
no code implementations • 15 Oct 2021 • Samuel Dooley, Ryan Downing, George Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P Dickerson, Tom Goldstein
Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceived gender, skin type, lighting condition, etc.
1 code implementation • 27 Aug 2021 • 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.
1 code implementation • NeurIPS 2021 • Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson
In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs.
no code implementations • 13 Oct 2020 • Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson
Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
no code implementations • 24 Sep 2020 • Samuel Dooley, John P. Dickerson
We model this affiliate matching problem in a generalization of the classic stable marriage setting by permitting firms to state preferences over not just which workers to whom they are matched, but also to which firms their affiliated workers are matched.
1 code implementation • 17 Jun 2020 • Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson
In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.
no code implementations • 7 Aug 2018 • Eliza Mace, Keith Manville, Monica Barbu-McInnis, Michael Laielli, Matthew Klaric, Samuel Dooley
Specifically, we examine how various features of the data affect building detection accuracy with respect to the Intersection over Union metric.
2 code implementations • 22 Feb 2018 • Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, Brendan McCord
We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research.