Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR

TL;DR

This paper discusses the limitations of current explainable AI approaches under GDPR and proposes a user-centered explanatory AI framework that enables interactive, tailored explanations for automated decision-making systems.

Contribution

It introduces the concept of explanatorY AI (YAI) as an extension of XAI, focusing on organizing explanations into user-centered narratives for better compliance and understanding.

Findings

Current XAI approaches are insufficient for GDPR requirements.

YAI enables interactive, personalized explanations for users.

The proposed framework improves user engagement with AI explanations.

Abstract

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.