Explainability and justification of automatic-decision making: A conceptual framework and a practical application

TL;DR

This paper develops a conceptual framework distinguishing explanation from justification in algorithmic decision-making, integrating theories from philosophy and law, and demonstrates its application through a university admissions case study.

Contribution

It introduces a novel framework based on Habermas and Perelman's theories to analyze explanations and justifications in automated decisions, enhancing understanding of their acceptability.

Findings

Framework clarifies how explanations support justification.

Case study illustrates practical application in university admissions.

Differentiates explanation from justification for better transparency.

Abstract

Explainability of algorithmic decision-making systems is both a regulatory objective and an area of intense research. The article argues that a crucial condition for the acceptability of algorithmic decision-making systems is that decisions must be justified in the eyes of their recipients. We make a clear distinction between explanation and justification. Explanations describe how a decision was made, while justifications give reasons that aim to make the decision acceptable. We propose a conceptual framework of explanations and justifications, based on Habermas's theory of communicative action and Perelman's New Rhetoric theory of law. This framework helps to analyze how different forms of explanation can support or fail to support justification. We illustrate our approach with a case study on university admissions in France.