# RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems

arXiv: https://arxiv.org/abs/2306.01774 · HTML: https://mlsanity.com/paper/2306.01774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 5 of 5)

- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [ECCOLA -- a Method for Implementing Ethically Aligned AI Systems](https://mlsanity.com/paper/2004.08377/) (2020)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (2022)
- [An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications](https://mlsanity.com/paper/2301.13476/) (2023)

## Built on by (most cited first, 4 of 4)

- [Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering](https://mlsanity.com/paper/2505.04251/) (2025)
- [Prompt Smells: An Omen for Undesirable Generative AI Outputs](https://mlsanity.com/paper/2401.12611/) (2024)
- [A five-layer framework for AI governance: integrating regulation, standards, and certification](https://mlsanity.com/paper/2509.11332/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)

## Abstract (arXiv)

Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them. Furthermore, requirements engineering (RE), which is identified to foster trustworthiness in the AI development process from the early stages was observed to be absent in a lot of frameworks that support the development of ethical and trustworthy AI. This incongruous phrasing combined with a lack of concrete development practices makes trustworthy AI development harder. To address this concern, we formulated a comparison table for the terminology used and the coverage of the ethical AI principles in major ethical AI guidelines. We then examined the applicability of ethical AI development frameworks for performing effective RE during the development of trustworthy AI systems. A tertiary review and meta-analysis of literature discussing ethical AI frameworks revealed their limitations when developing trustworthy AI. Based on our findings, we propose recommendations to address such limitations during the development of trustworthy AI.