From Questions to Insights: Exploring XAI Challenges Reported on Stack Overflow Questions

Model integration is the most severe XAI challenge practitioners face: The study of 663 Stack Overflow questions found that integrating XAI techniques with existing AI models emerged as both the most prevalent challenge and the most severe based on answer metadata, suggesting this is a critical barrier to real-world XAI adoption.

Disagreement issues significantly undermine trust in AI explanations: The research identified "disagreement issues" (where XAI techniques produce conflicting or inconsistent explanations) as the second most prevalent challenge, and practitioners reported this type of issue most severely affects their ability to use XAI techniques effectively—a concerning finding for responsible AI since inconsistent explanations could erode user confidence in AI systems.

Seven distinct categories of XAI challenges exist in practice: Beyond model integration and disagreement issues, the study cataloged five additional challenges that practitioners encounter, indicating that XAI accessibility problems are multifaceted and require targeted solutions rather than one-size-fits-all approaches.

Current XAI techniques like SHAP and LIME have significant usability gaps: The prevalence of questions about these popular explainability methods on Stack Overflow reveals that despite their theoretical promise, these tools create practical friction for real-world implementation, suggesting the need for more user-friendly XAI solutions.

Practitioner feedback should guide XAI tool design: The study's inclusion of practitioner responses about which challenges most affect their work demonstrates that responsible AI development requires direct input from those actually deploying these systems, not just theoretical considerations.