Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy.Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments.Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy.Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging tradeoffs.
We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)-an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to explicitly spell out the moral assumptions underlying each notion of fairness, and interpret recent fairness impossibility results in a new light. Last but not least and inspired by luck egalitarian models of EOP, we propose a new family of measures for algorithmic fairness. We illustrate our proposal empirically and show that employing a measure of algorithmic (un)fairness when its underlying moral assumptions are not satisfied, can have devastating consequences for the disadvantaged group's welfare. Policy Predictive model h Effort e Effort-based utility d Circumstance c Irrelevant features z Utility u Actual -effort-based utility (a -d) Economic Models of EOP Fair Machine LearningFigure 1: Our proposed conceptual mapping between Fair ML and economic literature on EOP.factors-those that can morally justify inequality. (Prior work in economics refers to e as effort for the sake of concreteness, but e summarizes all factors for which the individual can be held morally accountable; the term "effort" should not be interpreted in its ordinary sense here.) For any circumstance c and any effort level e, a policy φ induces a distribution of utility among people of circumstance c and effort e. Formally, an EOP policy will ensure that an individual's final utility will be, to the extent possible, only a function of their effort and not their circumstances. While EOP has been traditionally discussed in the context of employment practices, its scope has been expanded over time to other areas, including lending, housing, college admissions, and beyond [Wikipedia, 2018]. Decisions made in such domains are increasingly automated and made through Algorithmic Data Driven Decision Making systems (A3DMs). We argue, therefore, that it is only natural to study fairness for A3DMs through the lens of EOP. In this work, we draw a formal connection between the recently proposed notions of fairness for supervised learning and economic models of EOP. We observe that in practice, predictive models inevitably make errors (e.g. the model may mistakenly predict that a credit-worthy applicant won't pay back their loan in time). Sometimes these errors are beneficial to the subject, and sometimes they cause harm. We posit that in this context, EOP would require similar individuals (in terms of what they can be held accountable for) to have the same prospect of receiving this benefit/harm, irrespective of their irrelevant characteristics.More precisely, we assume that a person's features can be partitioned...
Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has dealt with these impossibility results by quantifying the tradeoffs between different formulations of fairness. Our work takes a different perspective on this issue. Rather than requiring all notions of fairness to (partially) hold at the same time, we ask which one of them is the most appropriate given the societal domain in which the decision-making model is to be deployed. We take a descriptive approach and set out to identify the notion of fairness that best captures lay people's perception of fairness. We run adaptive experiments designed to pinpoint the most compatible notion of fairness with each participant's choices through a small number of tests. Perhaps surprisingly, we find that the most simplistic mathematical definition of fairnessnamely, demographic parity-most closely matches people's idea of fairness in two distinct application scenarios. This conclusion remains intact even when we explicitly tell the participants about the alternative, more complicated definitions of fairness, and we reduce the cognitive burden of evaluating those notions for them. Our findings have important implications for the Fair ML literature and the discourse on formalizing algorithmic fairness.
We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine which of the two is more unfair? Our core idea is to use existing inequality indices from economics to measure how unequally the outcomes of an algorithm benefit different individuals or groups in a population. Our work offers a justified and general framework to compare and contrast the (un)fairness of algorithmic predictors. This unifying approach enables us to quantify unfairness both at the individual and the group level. Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component. Earlier methods are typically designed to tackle only between-group unfairness, which may be justified for legal or other reasons. However, we demonstrate that minimizing exclusively the between-group component may, in fact, increase the within-group, and hence the overall unfairness. We characterize and illustrate the tradeoffs between our measures of (un)fairness and the prediction accuracy.
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