Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI

TL;DR

This paper proposes a framework to evaluate explainable AI tools in their ability to detect bias and fairness issues, highlighting current limitations and guiding improvements to prevent fairwashing.

Contribution

It introduces a novel framework for assessing XAI tools' effectiveness in fairness detection and communication, addressing a critical gap in current explainability methods.

Findings

Many XAI tools lack bias detection features

Framework helps identify modifications to improve fairness communication

Guides developers to reduce fairwashing risks

Abstract

Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a tool for `fairwashing` by misleading users into trusting biased or incorrect models. In this paper, we created a framework for evaluating explainable AI tools with respect to their capabilities for detecting and addressing issues of bias and fairness as well as their capacity to communicate these results to their users clearly. We found that despite their capabilities in simplifying and explaining model behavior, many prominent XAI tools lack features that could be critical in detecting bias. Developers can use our framework to suggest modifications needed in their toolkits to reduce issues likes fairwashing.