Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems
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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.
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
9 of 88 references.
References · 9
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
- 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
- 2019Requirements Engineering for Machine Learning: Perspectives from Data Scientistscited by 49
- 2019Requirements Engineering Challenges in Building AI-Based Complex Systemscited by 21
- 2019Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Componentscited by 18
- 2018Effect of Depth and Width on Local Minima in Deep Learningcited by 12
- 2023Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systemscited by 8
- 2019Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systemscited by 7
- 2019A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018cited by 4
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Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.
7 papers.
- 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practicecited by 6
- 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directionscited by 5
- 2023An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AIcited by 3
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 3
- 2024Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshopcited by 1
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systemscited by 1
- 2026From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systemscited by 0
- 2026From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems30 Jun 2026
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systems11 Jul 2025
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models23 Apr 2025
- 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice20 Feb 2025
- 2024Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop9 Nov 2024
- 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions11 Sep 2024
- 2023An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI27 Nov 2023