mlsanityData to 7 Oct 2026

Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements

arXiv 2206.01507 ↗First posted 3 Jun 2022cs.SE cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems. Explanations help to reduce system opacity, support transparency, and increase stakeholder trust. In this position paper, we discuss synergies between requirements engineering (RE) and Explainable AI (XAI). We highlight challenges in the field of XAI, and propose a framework and research directions on how RE practices can help to mitigate these challenges.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

10 of 36 references.

References · 10

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  5. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  6. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  7. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  8. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  9. 2020Interpretability and Explainability: A Machine Learning Zoo Mini-tourcited by 55
  10. 2021Designer-User Communication for XAI: An epistemological approach to discuss XAI designcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directionscited by 5
  2. 2023Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goalscited by 4
  3. 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Designcited by 2
  4. 2026Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Trainingcited by 0