# Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2301.10404 · HTML: https://mlsanity.com/paper/2301.10404/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2023.

## Built on by (most cited first, 8 of 8)

- [Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems](https://mlsanity.com/paper/2303.02920/) (2023)
- [AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems](https://mlsanity.com/paper/2311.14695/) (2023)
- [A Vision for Operationalising Diversity and Inclusion in AI](https://mlsanity.com/paper/2312.06074/) (2023)
- [Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products](https://mlsanity.com/paper/2503.17955/) (2025)
- [Towards Requirements Engineering for RAG Systems](https://mlsanity.com/paper/2505.07553/) (2025)
- [Enabling Human-Centered AI: A Methodological Perspective](https://mlsanity.com/paper/2311.06703/) (2023)
- [Understanding Practitioners Perspectives on Monitoring Machine Learning Systems](https://mlsanity.com/paper/2509.25195/) (2025)
- [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026)

## Abstract (arXiv)

[Context] Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google, Microsoft, and Apple have provided a suite of recent guidelines to assist engineering teams in building human-centered AI systems. [Objective] The practices currently adopted by practitioners for developing such systems, especially during Requirements Engineering (RE), are little studied and reported to date. [Method] This paper presents the results of a survey conducted to understand current industry practices in RE for AI (RE4AI) and to determine which key human-centered AI guidelines should be followed. Our survey is based on mapping existing industrial guidelines, best practices, and efforts in the literature. [Results] We surveyed 29 professionals and found most participants agreed that all the human-centered aspects we mapped should be addressed in RE. Further, we found that most participants were using UML or Microsoft Office to present requirements. [Conclusion] We identify that most of the tools currently used are not equipped to manage AI-based software, and the use of UML and Office may pose issues to the quality of requirements captured for AI. Also, all human-centered practices mapped from the guidelines should be included in RE.