Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
TLDR
In this article , the authors use equality of opportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness, and provide a moral interpretation of the impossibility results as the incompatibility between different notions of a fair contest when people do not have fair life chances.Abstract:
In this work we use Equal Opportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people’s fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest — foward-facing versus backward-facing — when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck egalitarian doctrine of EO, and Rawls’s principle of fair equality of opportunity.read more
Citations
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Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity
TL;DR: In this article , the authors focus on the wider network of decisions that determine which opportunities are allocated to whom and recommend prioritizing regulatory and design-based interventions that alleviate severe bottlenecks in order to expand access to opportunities in a pluralistic way.
Proceedings ArticleDOI
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell,Lucius Bynum,Nazarii Drushchak,Tetiana Herasymova,Lucas Rosenblatt,Julia Stoyanovich +5 more
TL;DR: In this article , the authors argue that the impossibility theorem does not always translate to practice, and they show that achieving fairness along multiple metrics for multiple groups is much more possible than was previously believed.
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The Flawed Foundations of Fair Machine Learning
TL;DR: In this article , the authors show that there is a tradeoff between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist, and that the trade-off presents an existential threat to the equitable, fair machine learning approach.
Proceedings ArticleDOI
Fairness in Ranking: From Values to Technical Choices and Back
TL;DR: There has been much work on incorporating fairness requirements into the design of algorithmic rankers, with contributions from the data management, algorithms, information retrieval, and recommender systems communities as discussed by the authors .
References
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Proceedings Article
Equality of opportunity in supervised learning
TL;DR: This work proposes a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features and shows how to optimally adjust any learned predictor so as to remove discrimination according to this definition.
Proceedings ArticleDOI
Fairness through awareness
TL;DR: A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
Journal ArticleDOI
What Is the Point of Equality
TL;DR: The authors argues that the problems stem from a flawed understanding of the point of equality and argues that in focusing on correcting a supposed cosmic injustice, egalitarian writing has lost sight of the distinctively political aims of egalitarianism.
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Fairness Through Awareness
TL;DR: In this article, the authors proposed a framework for fair classification comprising a task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand, and an algorithm for maximizing utility subject to the fairness constraint that similar individuals are treated similarly.
Journal ArticleDOI
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
TL;DR: It is demonstrated that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups, and how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.