Explainable Artificial Intelligence

7.

Evaluation of Explanations

7.1.

Quantitative Evaluation Metrics

7.1.1.

Fidelity Measures

7.1.1.1.

Local Fidelity Assessment

7.1.1.2.

Global Fidelity Assessment

7.1.1.3.

Approximation Quality Metrics

7.1.2.

Stability and Robustness

7.1.2.1.

Input Perturbation Sensitivity

7.1.2.2.

Explanation Consistency

7.1.2.3.

Lipschitz Continuity

7.1.3.

7.1.3.1.

Feature Coverage

7.1.3.2.

Explanation Comprehensiveness

7.1.4.

Compactness and Sparsity

7.1.4.1.

Number of Features

7.1.4.2.

Information Density

7.1.5.

7.1.5.1.

Discriminative Power

7.1.5.2.

Foil Comparison Quality

7.2.

Human-Centered Evaluation

7.2.1.

User Studies Design

7.2.1.1.

Experimental Design Principles

7.2.1.2.

Control Group Selection

7.2.1.3.

Bias Mitigation Strategies

7.2.2.

Subjective Satisfaction Measures

7.2.2.1.

Perceived Usefulness

7.2.2.2.

Explanation Quality Ratings

7.2.2.3.

User Preference Studies

7.2.3.

Objective Performance Measures

7.2.3.1.

Task Accuracy Improvement

7.2.3.2.

Decision Time Analysis

7.2.3.3.

Error Rate Reduction

7.2.4.

Trust and Confidence Assessment

7.2.4.1.

Trust Calibration

7.2.4.2.

Confidence Alignment

7.2.4.3.

Long-term Trust Evolution

7.2.5.

Mental Model Evaluation

7.2.5.1.

Model Understanding Assessment

7.2.5.2.

Misconception Detection

7.2.5.3.

Learning Effectiveness

7.2.6.

Cognitive Load Assessment

7.2.6.1.

Working Memory Demands

7.2.6.2.

Processing Time Requirements

7.2.6.3.

Attention Allocation

7.3.

Evaluation Frameworks and Protocols

7.3.1.

Benchmark Datasets

7.3.1.1.

Standard Evaluation Tasks

7.3.1.2.

Ground Truth Establishment

7.3.2.

Evaluation Methodologies

7.3.2.1.

A/B Testing Approaches

7.3.2.2.

Longitudinal Studies

7.3.2.3.

Cross-Domain Validation

7.3.3.

Metrics Standardization

7.3.3.1.

Community Standards

7.3.3.2.

Reproducibility Requirements

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8. Practical Applications of XAI