mlsanityData to 14 Oct 2026

Tailoring Requirements Engineering for Responsible AI

arXiv 2302.10816 ↗First posted 21 Feb 2023cs.AI cs.SEcited by 7

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Abstract (arXiv)

Requirements Engineering (RE) is the discipline for identifying, analyzing, as well as ensuring the implementation and delivery of user, technical, and societal requirements. Recently reported issues concerning the acceptance of Artificial Intelligence (AI) solutions after deployment, e.g. in the medical, automotive, or scientific domains, stress the importance of RE for designing and delivering Responsible AI systems. In this paper, we argue that RE should not only be carefully conducted but also tailored for Responsible AI. We outline related challenges for research and practice.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 8 of its 36 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

8 of 36 references.

References · 8

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  3. 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
  4. 2019Requirements Engineering for Machine Learning: Perspectives from Data Scientistscited by 49
  5. 2021Exploring Explainability: A Definition, a Model, and a Knowledge Cataloguecited by 15
  6. 2021Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industrycited by 9
  7. 2022Efficient, Uncertainty-based Moderation of Neural Networks Text Classifierscited by 7
  8. 2022How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Studycited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 1.

7 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. 2023Responsible Design Patterns for Machine Learning Pipelinescited by 4
  3. 2023Quality Issues in Machine Learning Software Systemscited by 0
  4. 2025Expanding ML-Documentation Standards For Better Securitycited by 0
  5. 2025Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Developmentcited by 0
  6. 2025Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Studycited by 0
  7. 2026From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systemscited by 0