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05/29/2023
The Misuse of AUC: What High Impact Risk Assessment Gets Wrong
When determining which machine learning model best performs some high im...
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05/30/2022
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
Voter suppression and associated racial disparities in access to voting ...
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06/10/2021
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Risk assessment instrument (RAI) datasets, particularly ProPublica's COM...
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04/24/2021
Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability
When it comes to studying the impacts of decision making, the research h...
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01/04/2021
A Research Ecosystem for Secure Computing
Computing devices are vital to all areas of modern life and permeate eve...
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12/11/2020
Interdisciplinary Approaches to Understanding Artificial Intelligence's Impact on Society
Innovations in AI have focused primarily on the questions of "what" and ...
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07/02/2020
Evolving Methods for Evaluating and Disseminating Computing Research
Social and technical trends have significantly changed methods for evalu...
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06/19/2020
Fair clustering via equitable group representations
What does it mean for a clustering to be fair? One popular approach seek...
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02/25/2020
Problems with Shapley-value-based explanations as feature importance measures
Game-theoretic formulations of feature importance have become popular as...
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09/07/2019
Equalizing Recourse across Groups
The rise in machine learning-assisted decision-making has led to concern...
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06/20/2019
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Motivated by the need to audit complex and black box models, there has b...
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01/28/2019
Fairness in representation: quantifying stereotyping as a representational harm
While harms of allocation have been increasingly studied as part of the ...
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02/20/2018
Sublinear Algorithms for MAXCUT and Correlation Clustering
We study sublinear algorithms for two fundamental graph problems, MAXCUT...
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02/13/2018
A comparative study of fairness-enhancing interventions in machine learning
Computers are increasingly used to make decisions that have significant ...
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07/03/2017
Fair Pipelines
This work facilitates ensuring fairness of machine learning in the real ...
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06/29/2017
Runaway Feedback Loops in Predictive Policing
Predictive policing systems are increasingly used to determine how to al...
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09/23/2016
On the (im)possibility of fairness
What does it mean for an algorithm to be fair? Different papers use diff...
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03/04/2016
A Unified View of Localized Kernel Learning
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting...
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02/23/2016
Auditing Black-box Models for Indirect Influence
Data-trained predictive models see widespread use, but for the most part...
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04/08/2015
A Group Theoretic Perspective on Unsupervised Deep Learning
Why does Deep Learning work? What representations does it capture? How d...
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12/20/2014
Why does Deep Learning work? - A perspective from Group Theory
Why does Deep Learning work? What representations does it capture? How d...
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12/11/2014
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentio...
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06/14/2013
Rethinking Abstractions for Big Data: Why, Where, How, and What
Big data refers to large and complex data sets that, under existing appr...
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06/25/2012
A Geometric Algorithm for Scalable Multiple Kernel Learning
We present a geometric formulation of the Multiple Kernel Learning (MKL)...
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04/16/2012
Efficient Protocols for Distributed Classification and Optimization
In distributed learning, the goal is to perform a learning task over dat...
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02/27/2012
Protocols for Learning Classifiers on Distributed Data
We consider the problem of learning classifiers for labeled data that ha...
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03/02/2010
A Unified Algorithmic Framework for Multi-Dimensional Scaling
In this paper, we propose a unified algorithmic framework for solving ma...
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01/04/2010
Comparing Distributions and Shapes using the Kernel Distance
Starting with a similarity function between objects, it is possible to d...
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10/05/2003