# Fairness through awareness

@article{Dwork2011FairnessTA, title={Fairness through awareness}, author={Cynthia Dwork and Moritz Hardt and Toniann Pitassi and Omer Reingold and Richard S. Zemel}, journal={ArXiv}, year={2011}, volume={abs/1104.3913} }

We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). [] Key Method The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the…

## 2,443 Citations

### Temporal Aspects of Individual Fairness

- Computer Science, EconomicsArXiv
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Two definitions of fairness-across-time can have drastically different implications in the setting where the principal needs to learn the utility model: one can achieve a vanishing asymptotic loss in long-run average utility relative to the full-information optimum under the fairness-in-hindsight constraint, whereas this asymPTotic loss can be bounded away from zero under the fair-ACross- time constraint.

### Metric Learning for Individual Fairness

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This work proposes a solution to the problem of approximating a metric for Individual Fairness based on human judgments by assuming that the arbiter can answer a limited set of queries concerning similarity of individuals for a particular task, is free of explicit biases and possesses sufficient domain knowledge to evaluate similarity.

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This work investigates whether privacy and fairness can be simultaneously achieved by a single classifier in several different models and gives an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.

### Eliciting and Enforcing Subjective Individual Fairness

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A framework for fairness elicitation is considered, in which fairness is indirectly specified only via a sample of pairs of individuals who should be treated (approximately) equally on the task, and a provably convergent oracle-efficient algorithm is provided for minimizing error subject to the fairness constraints.

### What's Fair about Individual Fairness?

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It is suggested that individual fairness cannot be a definition of fairness, and instead should be seen as one tool among several for ameliorating algorithmic bias.

### Fair for All: Best-effort Fairness Guarantees for Classification

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A fairness notion whose guarantee, on each group g in a class G, is relative to the performance of the best classifier on g is proposed, which applies to broad classes of groups, in particular, where G consists of all possible groups (subsets) in the data, and G is more streamlined.

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This paper is a survey of fairness notions that addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?".

### Distributional Individual Fairness in Clustering

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This paper adopts the individual fairness notion, which mandates that similar individuals should be treated similarly for clustering problems, and introduces a framework for assigning individuals, embedded in a metric space, to probability distributions over a bounded number of cluster centers.

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Fairness-across-time and fairness-in-hindsight are introduced and a new algorithm is designed, Cautious Fair Exploration (CAFE), which satisfies FH and achieves sub-linear regret guarantees for a broad range of settings.

### The cost of fairness in binary classification

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This work relates two existing fairness measures to cost-sensitive risks, and shows that for such costsensitive fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function.

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