Fair machine learning is receiving an increasing attention in machine
learning fields. Researchers in fair learning have developed correlation or
association-based measures such as demographic disparity, mistreatment
disparity, calibration, causal-based measures such as total effect, direct and
indirect discrimination, and counterfactual fairness, and fairness notions such
as equality of opportunity and equal odds that consider both decisions in the
training data and decisions made by predictive models. In this paper, we
develop a new causal-based fairness notation, called equality of effort.
Different from existing fairness notions which mainly focus on discovering the
disparity of decisions between two groups of individuals, the proposed equality
of effort notation helps answer questions like to what extend a legitimate
variable should change to make a particular individual achieve a certain
outcome level and addresses the concerns whether the efforts made to achieve
the same outcome level for individuals from the protected group and that from
the unprotected group are different. We develop algorithms for determining
whether an individual or a group of individuals is discriminated in terms of
equality of effort. We also develop an optimization-based method for removing
discriminatory effects from the data if discrimination is detected. We conduct
empirical evaluations to compare the equality of effort and existing fairness
notion and show the effectiveness of our proposed algorithms.

本文提出了一种新的基于因果关系的公平判定方法：努力平等。与现有的公平概念不同的是，努力平等不仅关注于两个群体之间的决策差异，而且还帮助回答了当特定个体实现特定结果水平时应合理变化的合法性变量的程度，以及保护组和无保护组个体实现相同结果水平所做的努力是否不同等问题。研究团队开发了算法来确定在平等的努力方面是否存在歧视，并发展了一种基于优化的方法，用来消除数据中的歧视影响。而实验结果表明，与现有的公平概念相比，努力平等的算法更为有效。