Auditing ML Models for Individual Bias and Unfairness

2020-03-11 · Songkai Xue, Mikhail Yurochkin, Yuekai Sun

We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal value. Our tools permit us to obtain asymptotic confidence intervals and hypothesis tests that cover the target/control the Type I error rate exactly. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.

📄 PDF Abstract BibTeX arXiv:2003.05048

Code (0)

등록된 구현이 없습니다.

Individual Fairness in Pipelines

2020-04-12 · Cynthia Dwork, Christina Ilvento, Meena Jagadeesan

It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary…

Fairness General Classification

Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors

2024-08-24 · Xueying Ding, Rui Xi, Leman Akoglu

The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised o…

Fairness Outlier Detection

FairEM360: A Suite for Responsible Entity Matching

2024-04-10 · Nima Shahbazi, Mahdi Erfanian, Abolfazl Asudeh, Fatemeh Nargesian 외

Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the dat…

Fairness

Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Data

2026-01-21 · Hongbo Bo, Jingyu Hu, Debbie Watson, Weiru Liu arxiv

The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues pe…

Equity of Attention: Amortizing Individual Fairness in Rankings

2018-05-04 · Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum

Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention…

Fairness Recommendation Systems