Search Results for author:
Found 9 papers, 4 papers with code
Matched Pair Calibration for Ranking Fairness
We propose a test of fairness in score-based ranking systems called matched pair calibration.
Adaptive Sampling Strategies to Construct Equitable Training Datasets
In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups.
Two-sided fairness in rankings via Lorenz dominance
Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.
Online certification of preference-based fairness for personalized recommender systems
We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users.
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
We discuss how we disentangle normative questions of product and policy design (like, "how should the system trade off between different stakeholders' interests and needs?")
The Measure and Mismeasure of Fairness
The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable.
Computers and Society
A large-scale analysis of racial disparities in police stops across the United States
We find that black drivers are stopped more often than white drivers relative to their share of the driving-age population, but that Hispanic drivers are stopped less often than whites.
Applications
Fast Threshold Tests for Detecting Discrimination
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions.
Enriching Object Detection With 2D-3D Registration and Continuous Viewpoint Estimation
We propose an efficient method for synthesizing templates from 3D models that runs on the fly -- that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage.
