Review of Mathematical frameworks for Fairness in Machine Learning

Contrastive Fairness in Machine Learning

Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are the questions that must be answered now that many decisions in real life can be made through machine learning. However research in fairness of algorithms has focused on the counterfactual questions "what if?" or "why?", whereas in real life most subjective questions of consequence…

Within-group fairness: A guidance for more sound between-group fairness

As they have a vital effect on social decision-making, AI algorithms not only should be accurate and but also should not pose unfairness against certain sensitive groups (e.g., non-white, women). Various specially designed AI algorithms to ensure trained AI models to be fair between sensitive groups have been developed. In this paper, we raise a new issue that between-group fair AI models could treat individuals in a same sensitive group unfairly. We introduce a new concept of fairness so-called within-group fairness which requires that AI models should be fair for those in a same sensitive gr…

需求学习下情境动态定价中的效用公平性

Utility Fairness in Contextual Dynamic Pricing with Demand Learning

本文研究需求不确定下带效用公平性约束的情境动态定价，提出新算法并给出最优遗憾上界，同时刻画最优策略结构并建立非标准遗憾下界，揭示公平性对效用与收入平衡的影响。

什么是公平？定义机器学习在健康领域的公平性

What is Fair? Defining Fairness in Machine Learning for Health

本文探讨健康领域机器学习中公平性的定义，分析不公平决策成因及衡量方法，综述群体、个体和因果框架下的公平性概念，并展望未来研究机遇与挑战。

Dynamic fairness - Breaking vicious cycles in automatic decision making

In recent years, machine learning techniques have been increasingly applied in sensitive decision making processes, raising fairness concerns. Past research has shown that machine learning may reproduce and even exacerbate human bias due to biased training data or flawed model assumptions, and thus may lead to discriminatory actions. To counteract such biased models, researchers have proposed multiple mathematical definitions of fairness according to which classifiers can be optimized. However, it has also been shown that the outcomes generated by some fairness notions may be unsatisfactory. I…

Measuring and signing fairness as performance under multiple stakeholder distributions

As learning machines increase their influence on decisions concerning human lives, analyzing their fairness properties becomes a subject of central importance. Yet, our best tools for measuring the fairness of learning systems are rigid fairness metrics encapsulated as mathematical one-liners, offer limited power to the stakeholders involved in the prediction task, and are easy to manipulate when we exhort excessive pressure to optimize them. To advance these issues, we propose to shift focus from shaping fairness metrics to curating the distributions of examples under which these are computed…