Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study
This addresses the problem of XAI being developer-focused rather than user-friendly, potentially improving adoption and effectiveness for end-users like medical professionals, though it is incremental as it builds on existing XAI research.
The study investigated how different types of explainable AI (XAI) explanations affect end-users, specifically 271 prospective physicians in a COVID-19 use case, finding that explanation types strongly influence cognitive load, task performance, and task time, with local explanations ranked best for mental efficiency.
While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research.