Transcending XAI Algorithm Boundaries through End-User-Inspired Design

TL;DR

This paper advocates for a shift in XAI research towards end-user needs, proposing new technical problems and evaluation metrics to enhance explainability for non-technical users in high-stakes domains.

Contribution

It introduces four novel XAI technical problems inspired by end-user requirements and discusses open challenges for developing user-centered explainability methods.

Findings

Identified four new XAI problems: edge-case reasoning, customizable counterfactuals, collapsible decision trees, verifiability metrics.

Bridged human-centered XAI with technical XAI, emphasizing the importance of user-focused explainability.

Highlighted open problems and future directions for responsible AI in critical applications.

Abstract

The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability. This research paradigm disproportionately ignores the larger group of non-technical end users, who have a much higher demand for AI explanations in diverse explanation goals, such as making safer and better decisions and improving users' predicted outcomes. Lacking explainability-focused functional support for end users may hinder the safe and accountable use of AI in high-stakes domains, such as healthcare, criminal justice, finance, and autonomous driving systems. Built upon prior human factor analysis on end users' requirements for XAI, we identify and model four novel XAI technical problems covering the full spectrum from design to the evaluation of XAI algorithms, including edge-case-based reasoning, customizable counterfactual explanation, collapsible decision tree, and the verifiability metric to evaluate XAI utility. Based on these newly-identified research problems, we also discuss open problems in the technical development of user-centered XAI to inspire future research. Our work bridges human-centered XAI with the technical XAI community, and calls for a new research paradigm on the technical development of user-centered XAI for the responsible use of AI in critical tasks.