How to Evaluate Explainability? | XAI

The evaluation of explainability (XAI) refers to the process of assessing the effectiveness and quality of explanations provided by XAI techniques. The goal is to determine how well these techniques help users, developers, and stakeholders understand the decision-making process of Artificial Intelligence models. Proper evaluation is crucial to ensure that the explanations are informative, trustworthy, and truly enhance model interpretability.

Criteria used to evaluate explainability

There are a number of different criteria that can be used to evaluate explainability, including:

  1. Accuracy: The accuracy of an explanation refers to how well it reflects the actual decision-making process of the AI model.
  2. Coverage: The coverage of an explanation refers to how much of the model's decision-making process is explained.
  3. Complexity: The complexity of an explanation refers to how easy it is for humans to understand and interpret.
  4. Human-friendliness: The human-friendliness of an explanation refers to how well it is tailored to the needs of the intended audience.

Factors that considering when evaluating explainability

When evaluating the explainability of Artificial Intelligence models, several key factors should be considered. Firstly, the level of transparency and comprehensibility provided by the explanation is crucial. Explanations should be interpretable to users with varying levels of expertise. Secondly, the fidelity and accuracy of the explanations are essential to ensure that they faithfully represent the model's decision-making process. Thirdly, the scope of the explanation should be evaluated, considering whether it pertains to individual predictions or provides insights into the overall model behavior.

Moreover, the scalability and efficiency of the explainability methods are important, especially for large datasets and complex models. Moreover, the potential presence of biases in the explanations should be assessed to ensure fairness. Lastly, user feedback and perception of the explanations play a vital role in determining the effectiveness and usability of the AI system. Considering these factors leads to the selection of appropriate explainability methods that enhance transparency, trust, and interpretability of Artificial Intelligence models across various applications and domains.

Challenges in evaluating explainability

Here are some of the challenges in evaluating explainability:

  1. Lack of standardized evaluation metrics: There is no single, agreed-upon set of evaluation metrics for XAI methods. This can make it difficult to compare different XAI methods and to assess their effectiveness.
  2. Subjectivity of human evaluation: The evaluation of explainability is often subjective, meaning that different people may have different opinions on the quality of an explanation. This can make it difficult to reach a consensus on the quality of an explanation.
  3. Difficulty of measuring explainability: It can be difficult to measure the explainability of an AI model, as there is no single, agreed-upon definition of explainability.


Evaluation of explainability is an ongoing and evolving process, as new XAI techniques are developed and the field progresses. By conducting thorough evaluations, we can continuously improve the quality and effectiveness of explanations, making Artificial Intelligence systems more transparent, trustworthy, and accountable in various applications and domains.