# Tailoring Requirements Engineering for Responsible AI

arXiv: https://arxiv.org/abs/2302.10816 · HTML: https://mlsanity.com/paper/2302.10816/

## 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 (most cited first, 8 of 8)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue](https://mlsanity.com/paper/2108.03012/) (2021)
- [Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry](https://mlsanity.com/paper/2109.00872/) (2021)
- [Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers](https://mlsanity.com/paper/2204.01334/) (2022)
- [How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Study](https://mlsanity.com/paper/2204.01438/) (2022)

## Built on by (most cited first, 7 of 7)

- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [Responsible Design Patterns for Machine Learning Pipelines](https://mlsanity.com/paper/2306.01788/) (2023)
- [Quality Issues in Machine Learning Software Systems](https://mlsanity.com/paper/2306.15007/) (2023)
- [Expanding ML-Documentation Standards For Better Security](https://mlsanity.com/paper/2507.12003/) (2025)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)
- [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026)

## 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.