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

TL;DR

The paper investigates how to operationalize trustworthy AI for autonomous systems under the EU AI Act by evaluating ethical AI guidelines and development frameworks through an RE lens. It reveals inconsistencies in principle coverage and a lack of concrete RE guidance, then proposes eight RE-centered recommendations (R1–R8) to embed ethics early in the design process and to adapt to evolving guidelines. A meta-analysis of tertiary studies highlights practical gaps, notably in data governance, explicability, and trade-offs among principles. The work advances a path toward an RE-centric framework that supports trustworthy-by-design AI, with future work focused on framework validation in industrial settings and ongoing regulatory adaptation.

Abstract

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.

Figures (1)

• Figure 1: Research methodology employed to answer the three research questions.