Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation

TL;DR

This paper presents a comprehensive framework that integrates moral assessment and statistical criteria to develop fair prediction-based decision systems, extending the FEC principle to cover all group fairness types and account for morally irrelevant factors.

Contribution

It introduces a step-by-step procedure combining ethical analysis with statistical fairness criteria and extends the FEC principle to encompass all group fairness types and relaxations.

Findings

The framework covers independence, separation, and sufficiency fairness criteria.

Extended FEC accounts for morally irrelevant elements and relaxations.

Provides a method to incorporate fairness into optimal decision making.

Abstract

Ensuring fairness of prediction-based decision making is based on statistical group fairness criteria. Which one of these criteria is the morally most appropriate one depends on the context, and its choice requires an ethical analysis. In this paper, we present a step-by-step procedure integrating three elements: (a) a framework for the moral assessment of what fairness means in a given context, based on the recently proposed general principle of "Fair equality of chances" (FEC) (b) a mapping of the assessment's results to established statistical group fairness criteria, and (c) a method for integrating the thus-defined fairness into optimal decision making. As a second contribution, we show new applications of the FEC principle and show that, with this extension, the FEC framework covers all types of group fairness criteria: independence, separation, and sufficiency. Third, we introduce an extended version of the FEC principle, which additionally allows accounting for morally irrelevant elements of the fairness assessment and links to well-known relaxations of the fairness criteria. This paper presents a framework to develop fair decision systems in a conceptually sound way, combining the moral and the computational elements of fair prediction-based decision-making in an integrated approach. Data and code to reproduce our results are available at https://github.com/joebaumann/fair-prediction-based-decision-making.