The Ethical Requirements Stack: Operationalizing Adaptive Ethical Requirements with Human-AI Collaboration and GPT-Based LLMs

Abstract

The ongoing evolution and societal impact of AI systems demand systematic methods to embed ethics into AI development. Existing approaches often struggle to translate high-level ethical principles into concrete, adaptable software requirements, resulting in “ethical debt” that risks reputational harm, regulatory issues, and diminished stakeholder trust. This paper introduces the Ethical Requirements Stack (ERS), a structured, multi-layered artifact designed to elicit, decompose, and manage ethical requirements (ERs) from abstract themes to actionable development tasks. The ERS is operationalized through a human–AI collaborative workflow that leverages GPT-based Large Language Models (LLMs) for scalable ideation, complemented by human oversight to ensure contextual and ethical alignment. Using a design science research methodology, we demonstrate how the ERS supports the translation of stakeholder-elicited ethical values—aligned with frameworks such as IEEE 7000™-2021—into traceable software specifications. Our findings show that the ERS enables structured ethical reasoning and highlights the complementary strengths of AI-generated breadth and human critical judgment. This work contributes a practical approach for integrating ethics into the AI development lifecycle, supporting responsible innovation and reducing ethical debt through a combination of human-centered design and LLM-assisted requirements engineering.