The Ethical Requirements Stack: Operationalizing Adaptive Ethical Requirements with Human-AI Collaboration and GPT-Based LLMs
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© The Author(s) 2026. This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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.
Emojulkaisu
Software Business: 16th International Conference, ICSOB 2025, Stuttgart, Germany, November 24–26, 2025, Proceedings
ISBN
978-3-032-14518-5
ISSN
1865-1356
1865-1348
1865-1348
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Kausijulkaisu
Lecture notes in business information processing|574
OKM-julkaisutyyppi
A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Avainsanat
Ethical Requirements StackEthical requirementsAI ethicsEthical debtHuman–AI collaborationGPT‑based LLMsLarge Language ModelsDesign Science ResearchRequirements engineeringIEEE 7000‑2021Responsible AIHuman‑centered designStakeholder valuesAI‑assisted requirements engineeringEthical value operationalizationRequirements traceabilityAgile portfolio management