Fair Classification with Instance-dependent Label Noise

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. , \textitCOMPAS 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 \textitinstance-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 \textitcounterfactual fairness simultaneously. Experimental results demonstrate the effectiveness of the proposed methods on real-world datasets with controllable synthetic label noise.

Authors

• Songhua Wu
• Mingming Gong University of Melbourne Mohamed bin Zayed University of Artificial Intelligence
• Bo Han Hong Kong Baptist University
• Yang Liu College of Computer Science and Software Engineering, Shenzhen University
• Tongliang Liu Sydney AI Centre, The University of Sydney, Sydney

Keywords

No keywords are indexed for this paper.