Seamful XAI: Operationalizing Seamful Design in Explainable AI

AI mistakes are inevitable and often stem from hidden gaps between technical systems and real-world use, not just algorithmic flaws. Rather than concealing these imperfections, the research suggests revealing them can actually help users better understand and mitigate AI failures.

Showing users where AI systems "break down" can empower them more than hiding problems behind a seamless interface. The study found that when 43 AI practitioners and end-users were shown realistic scenarios of AI failures and their underlying causes, users could better anticipate harms and understand how to work around system limitations.

A structured design process called "Seamful XAI" helps teams identify and intentionally design around the gaps between AI systems and real-world contexts. By mapping where mismatches occur throughout an AI system's lifecycle, practitioners can create explanations that address root causes rather than just surface-level algorithmic opacity.

Transparency about AI limitations can increase user agency and trust more effectively than claiming seamless performance. When users understood why an AI system failed—whether due to technical constraints, data gaps, or organizational factors—they were better equipped to make informed decisions about relying on or correcting the system.

Responsible AI requires involving both technical practitioners and actual end-users in anticipating failures. The co-design approach revealed that real-world users often identify different failure modes and concerns than practitioners alone would predict, suggesting that responsible AI development needs diverse stakeholder input.