Fairness through Equality of Effort

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. Introduction Fair machine learning is receiving an increasing attention in machine learning fields. Discrimination is unfair treatment towards individuals based on the group to which they are perceived to belong. The first endeavor of the research community to achieve fairness is developing correlation or association-based measures, including demographic disparity (e.g., risk difference), mistreatment disparity, calibration, etc. (Romei and Ruggieri 2014; Luong, Ruggieri, and Turini 2011; ˇ Zliobaite, Kamiran, and Calders 2011; Dwork et al. 2012; Feldman et al. 2015), which mainly focus on discovering the disparity of certain statistical metrics between two groups of individuals. However, as paid increasing attention recently (Zhang, Wu, and Wu 2017b; Kilbertus et al. 2017; Nabi and Shpitser 2018), unlawful discrimination is a causal connection between the challenged decision and a protected characteristic, which cannot be captured by simple correlation or association concepts.