A Meta Survey of Quality Evaluation Criteria in Explanation Methods

Understand

Explanation methods and their evaluation have become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS).

• Since the most accurate AI models are opaque with low transparency and comprehensibility, explanations are essential for bias detection and control of uncertainty.
• There are a plethora of criteria to choose from when evaluating explanation method quality.
• However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods.