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Human-centered evaluation of explainable AI applications: a systematic review.

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Summary

This study reviews how users evaluate Explainable Artificial Intelligence (XAI). It identifies 30 components of meaningful explanations and calls for standardized user evaluation methods in XAI research.

Keywords:
XAIXAI evaluationexplainable AIhuman-AI interactionhuman-AI performancehuman-centered evaluationmeaningful explanationssystematic review

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Explainable Artificial Intelligence (XAI) seeks to demystify AI systems.
  • Explainability is increasingly recognized as a human-centric quality.
  • Current research lacks consensus on user evaluation in XAI.

Purpose of the Study:

  • To systematically review and synthesize the state of human-centered XAI evaluation.
  • To identify components that make AI explanations meaningful to users.
  • To propose a taxonomy for evaluating XAI from a user perspective.

Main Methods:

  • Systematic literature review of 73 papers on user-evaluated XAI.
  • Analysis of components contributing to explanation meaningfulness.
  • Categorization of evaluation components into a taxonomy.

Main Results:

  • Identified 30 components of meaningful explanations.
  • Developed a taxonomy based on explanation quality, human-AI interaction, and performance.
  • Found a significant lack of methodological standardization across XAI user studies.

Conclusions:

  • Understanding user perception is key to effective XAI.
  • A unified approach to XAI user evaluation is needed.
  • Standardized methodologies will enable better cross-study comparisons and advance XAI research.