Fair Classification with Instance-dependent Label Noise

Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)


With the widespread use of machine learning systems in our daily lives, it is important to consider fairness as a basic requirement when designing these systems, especially when the systems make life-changing decisions, e.g., \textit{COMPAS} algorithm helps judges decide whether to release an offender. For another thing, due to the cheap but imperfect data collection methods, such as crowdsourcing and web crawling, label noise is ubiquitous, which unfortunately makes fairness-aware algorithms even more prejudiced than fairness-unaware ones, and thereby harmful. To tackle these problems, we provide general frameworks for learning fair classifiers with \textit{instance-dependent label noise}. For statistical fairness notions, we rewrite the classification risk and the fairness metric in terms of noisy data and thereby build robust classifiers. For the causality-based fairness notion, we exploit the internal causal structure of data to model the label noise and \textit{counterfactual fairness} simultaneously. Experimental results demonstrate the effectiveness of the proposed methods on real-world datasets with controllable synthetic label noise.
Original languageEnglish
Title of host publicationProceedings of 1st Conference on Causal Learning and Reasoning, CLeaR 2022
EditorsBernhard Schölkopf, Caroline Uhler, Kun Zhang
PublisherConference on Causal Learning and Reasoning
Number of pages17
Publication statusPublished - Apr 2022
Event1st Conference on Causal Learning and Reasoning, CLeaR 2022 - Virtual, Eureka, CA, United States
Duration: 11 Apr 202213 Apr 2022

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498


Conference1st Conference on Causal Learning and Reasoning, CLeaR 2022
Country/TerritoryUnited States
CityEureka, CA
Internet address


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