# Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue

arXiv: https://arxiv.org/abs/2108.03012 · HTML: https://mlsanity.com/paper/2108.03012/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2021.

## Built on by (most cited first, 15 of 15)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022)
- [Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2307.14239/) (2023)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Tailoring Requirements Engineering for Responsible AI](https://mlsanity.com/paper/2302.10816/) (2023)
- [A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2307.14246/) (2023)
- [Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency maps](https://mlsanity.com/paper/2504.17023/) (2025)
- [Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review](https://mlsanity.com/paper/2505.02828/) (2025)
- [Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy](https://mlsanity.com/paper/2307.14232/) (2023)
- [Software Doping Analysis for Human Oversight](https://mlsanity.com/paper/2308.06186/) (2023)
- [Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle](https://mlsanity.com/paper/2404.18736/) (2024)
- [In defence of post-hoc explanations in medical AI](https://mlsanity.com/paper/2504.20741/) (2025)
- [Explainable AI: Definition and attributes of a good explanation for health AI](https://mlsanity.com/paper/2409.15338/) (2024)
- [Towards explainable decision support using hybrid neural models for logistic terminal automation](https://mlsanity.com/paper/2509.07577/) (2025)
- [Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.21306/) (2026)

## Abstract (arXiv)

The growing complexity of software systems and the influence of software-supported decisions in our society awoke the need for software that is transparent, accountable, and trustworthy. 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 incorporate this NFR into systems, we need to understand what explainability means from a software engineering perspective and how it impacts other quality aspects in a system. This allows for an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. Nevertheless, explainability is currently under-researched in the domain of requirements engineering and there is a lack of conceptual models and knowledge catalogues that support the requirements engineering process and system design. In this work, we bridge this gap by proposing a definition, a model, and a catalogue for explainability. They illustrate how explainability interacts with other quality aspects and how it may impact various quality dimensions of a system. To this end, we conducted an interdisciplinary Systematic Literature Review and validated our findings with experts in workshops.