# How to Evaluate Explainability? -- A Case for Three Criteria

arXiv: https://arxiv.org/abs/2209.00366 · HTML: https://mlsanity.com/paper/2209.00366/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2307.14239/) (2023)
- [A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2307.14246/) (2023)

## Abstract (arXiv)

The increasing complexity of software systems and the influence of software-supported decisions in our society have sparked the need for software that is safe, reliable, and fair. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. However, in order to develop explainable systems, we need to understand when a system satisfies this NFR. To this end, appropriate evaluation methods are required. However, the field is crowded with evaluation methods, and there is no consensus on which are the "right" ones. Much less, there is not even agreement on which criteria should be evaluated. In this vision paper, we will provide a multidisciplinary motivation for three such quality criteria concerning the information that systems should provide: comprehensibility, fidelity, and assessability. Our aim is to to fuel the discussion regarding these criteria, such that adequate evaluation methods for them will be conceived.