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arXiv · 2507.10228

Towards a Framework for Operationalizing the Specification of Trustworthy AI Requirements

Hugo Villamizar·Daniel Mendez·Marcos Kalinowski
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Abstract

Growing concerns around the trustworthiness of AI-enabled systems highlight the role of requirements engineering (RE) in addressing emergent, context-dependent properties that are difficult to specify without structured approaches. In this short vision paper, we propose the integration of two complementary approaches: AMDiRE, an artefact-based approach for RE, and PerSpecML, a perspective-based method designed to support the elicitation, analysis, and specification of machine learning (ML)-enabled systems. AMDiRE provides a structured, artefact-centric, process-agnostic methodology and templates that promote consistency and traceability in the results; however, it is primarily oriented toward deterministic systems. PerSpecML, in turn, introduces multi-perspective guidance to uncover concerns arising from the data-driven and non-deterministic behavior of ML-enabled systems. We envision a pathway to operationalize trustworthiness-related requirements, bridging stakeholder-driven concerns and structured artefact models. We conclude by outlining key research directions and open challenges to be discussed with the RE community.

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Hugo Villamizar, Daniel Mendez, Marcos Kalinowski. 2025-07-14. Towards a Framework for Operationalizing the Specification of Trustworthy AI Requirements. https://arxiv.org/abs/2507.10228

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