How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Study
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Abstract (arXiv)
Quality aspects such as ethics, fairness, and transparency have been proven to be essential for trustworthy software systems. Explainability has been identified not only as a means to achieve all these three aspects in systems, but also as a way to foster users' sentiments of trust. Despite this, research has only marginally focused on the activities and practices to develop explainable systems. To close this gap, we recommend six core activities and associated practices for the development of explainable systems based on the results of a literature review and an interview study. First, we identified and summarized activities and corresponding practices in the literature. To complement these findings, we conducted interviews with 19 industry professionals who provided recommendations for the development process of explainable systems and reviewed the activities and practices based on their expertise and knowledge. We compared and combined the findings of the interviews and the literature review to recommend the activities and assess their applicability in industry. Our findings demonstrate that the activities and practices are not only feasible, but can also be integrated in different development processes.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2022.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.
5 papers.
- 2023Tailoring Requirements Engineering for Responsible AIcited by 7
- 2023Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goalscited by 4
- 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Designcited by 2
- 2024Explainable AI: Definition and attributes of a good explanation for health AIcited by 0
- 2025The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPRcited by 0
- 2025The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR9 Jan 2025
- 2024Explainable AI: Definition and attributes of a good explanation for health AI9 Sep 2024
- 2023Tailoring Requirements Engineering for Responsible AI21 Feb 2023
- 2023Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals10 Feb 2023
- 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Design18 Aug 2022