Search Results for author: Samuel Dooley

Found 12 papers, 4 papers with code

A Deep Dive into Dataset Imbalance and Bias in Face Identification

no code implementations15 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.

Face Identification Face Recognition +1

Topological Data Analysis for Word Sense Disambiguation

no code implementations1 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.

Topological Data Analysis Word Embeddings +1

The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

no code implementations22 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.

Are Commercial Face Detection Models as Biased as Academic Models?

no code implementations25 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.

Face Detection Fairness

Comparing Human and Machine Bias in Face Recognition

no code implementations15 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.

Face Recognition

Robustness Disparities in Commercial Face Detection

1 code implementation27 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.

Face Detection

PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning

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.


ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

no code implementations13 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.


The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers

no code implementations24 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.

Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

1 code implementation17 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.

Decision Making Face Recognition +1

Overhead Detection: Beyond 8-bits and RGB

no code implementations7 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.

xView: Objects in Context in Overhead Imagery

2 code implementations22 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.

Object Detection

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