Core Concepts
Evaluation of explainable artificial intelligence requires a comprehensive framework that considers technical and social aspects.
Abstract
The content discusses the challenges in evaluating explainable artificial intelligence (XAI) systems. It proposes a sociotechnical utility-based evaluation framework to address deficiencies in current evaluation approaches. The framework emphasizes the need to recognize functionally independent components of XAI systems and evaluate them on multiple levels. It also highlights the importance of considering the operational context during validation to ensure effectiveness. The paper concludes by suggesting future work to expand and refine the proposed approach.
EVALUATION PURPOSES
- Lack of agreed-upon evaluation criteria for interpretability.
- Importance of user studies in assessing explanation quality.
- Challenges in evaluating XAI approaches due to sociotechnical nature.
EVALUATION APPROACHES
- Various metrics for evaluating XAI methods.
- Different types of evaluation frameworks based on XAI process aspects.
- Categorization based on computational vs. human interpretability.
EVALUATION DEFICIENCIES
- Inconsistent findings in XAI evaluation.
- Neglecting operational context in explainability assessment.
- Oversimplification of human explanatory processes in evaluations.
Stats
Predictive models come with well-defined performance metrics.
User studies are valuable for assessing explanation quality from users' perspective.
Sociotechnical nature complicates evaluation criteria agreement.
Quotes
"Explainability is not a property but an interactive communication process."
"XAI systems are often perceived as monolithic despite being highly modular."