The Case for Human-Centered XAI

This article examines the gap between current explainable AI (XAI) approaches and end-user needs, advocating for human-centered design that prioritizes user comprehension over technical sophistication in AI explanations.

Key Insights

• User Knowledge Gap: Study reveals that current XAI techniques work well for AI-experienced users but fail to provide meaningful explanations to users with low AI knowledge.
• Four Explanation Types: Research tested heatmap-based, example-based, concept-based, and prototype-based explanations using the Merlin bird identification app.
• Human-Centered Approach: Emphasizes the need for XAI techniques that serve end users rather than AI creators, requiring real user studies to validate explanation effectiveness.