# Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems

arXiv: https://arxiv.org/abs/2303.02920 · HTML: https://mlsanity.com/paper/2303.02920/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 88 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1908.01094/) (2019)
- [Effect of Depth and Width on Local Minima in Deep Learning](https://mlsanity.com/paper/1811.08150/) (2018)
- [Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems](https://mlsanity.com/paper/2301.10404/) (2023)
- [Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems](https://mlsanity.com/paper/1908.02134/) (2019)
- [A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018](https://mlsanity.com/paper/1907.07874/) (2019)

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

- [RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice](https://mlsanity.com/paper/2502.14930/) (2025)
- [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)
- [An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI](https://mlsanity.com/paper/2311.16027/) (2023)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop](https://mlsanity.com/paper/2411.06066/) (2024)
- [Multi-Agent LLMs as Ethics Advocates for AI-Based Systems](https://mlsanity.com/paper/2507.08392/) (2025)
- [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026)

## Abstract (arXiv)

[Context] Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions focus on technical aspects and ignore critical human-centered aspects. [Objective] Including human-centered aspects during requirements engineering (RE) when building AI-based software can help achieve more responsible, unbiased, and inclusive AI-based software solutions. [Method] In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software. We provide a catalog to elicit these requirements and a conceptual model to present them visually. [Results] The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360 degree~videos intended for virtual reality (VR) users. [Conclusion] We found that our proposed approach helped the project team fully understand the human-centered needs of the project to deliver. Furthermore, the framework helped to understand what requirements need to be captured at the initial stages against later stages in the engineering process of AI-based software.