Individual Fairness in Pipelines

Dwork, Cynthia; Ilvento, Christina; Jagadeesan, Meena

doi:10.4230/lipics.forc.2020.7

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Fair Machine Learning Through the Lens of Causality

Wu,

Zhang,

2023

Machine Learning for Causal Inference

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Fair Machine Learning Through the Lens of Causality

Wu,

Zhang,

2023

Machine Learning for Causal Inference

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Achieving Counterfactual Fairness for Causal Bandit

Huang

Zhang

2022

AAAI

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In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. By incorporating causal inference into bandits and adopting soft intervention to model the arm selection strategy, we first propose the d-separation based UCB algorithm (D-UCB) to explore the utilization of the d-separation set in reducing the amount of exploration needed to achieve low cumulative regret. Based on that, we then propose the fair causal bandit (F-UCB) for achieving the counterfactual individual fairness. Both theoretical analysis and empirical evaluation demonstrate effectiveness of our algorithms.

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Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

Wang

Thain

Sinha

et al. 2019

Preprint

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Most literature in fairness has focused on improving fairness with respect to one single model or one single objective. However, real-world machine learning systems are usually composed of many different components. Unfortunately, recent research has shown that even if each component is "fair", the overall system can still be "unfair" [16]. In this paper, we focus on how well fairness composes over multiple components in real systems. We consider two recently proposed fairness metrics for rankings: exposure and pairwise ranking accuracy gap. We provide theory that demonstrates a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a system's signals can achieve compositional fairness, and diagnosing which of these signals lowers the overall system's end-to-end fairness the most. Despite previously bleak theoretical results, on multiple data-sets-including a large-scale real-world recommender system-we find that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.Preprint. Under review.

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Individual Fairness in Pipelines

Cited by 4 publications

References 0 publications

Fair Machine Learning Through the Lens of Causality

Fair Machine Learning Through the Lens of Causality

Achieving Counterfactual Fairness for Causal Bandit

Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

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