# Metric Learning for Individual Fairness

## TL;DR

This paper introduces a method to approximate similarity metrics for individual fairness in classification by leveraging human judgments, enabling practical application of fairness guarantees without predefined metrics.

## Contribution

It proposes a framework for learning task-specific similarity metrics from limited human queries, including definitions, constructions, and learning procedures for generalization.

## Key findings

- Effective metric approximation from limited human queries
- Theoretical guarantees for generalization of learned metrics
- Practical approach to implement individual fairness in classification

## Abstract

There has been much discussion recently about how fairness should be measured or enforced in classification. Individual Fairness [Dwork, Hardt, Pitassi, Reingold, Zemel, 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it gives strong guarantees on treatment of individuals. Unfortunately, the need for a task-specific similarity metric has prevented its use in practice. In this work, we propose a solution to the problem of approximating a metric for Individual Fairness based on human judgments. Our model assumes that we have access to a human fairness arbiter, who 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. Our contributions include definitions for metric approximation relevant for Individual Fairness, constructions for approximations from a limited number of realistic queries to the arbiter on a sample of individuals, and learning procedures to construct hypotheses for metric approximations which generalize to unseen samples under certain assumptions of learnability of distance threshold functions.