Model Cards for Responsible AI: Stop Carding, Start Modelling
Kalvin Thuan-Phong Khuu, Nicolas Lacroix, Baptiste Lacroix, Richard F. Paige, Mireille Blay–Fornarino, Sébastien Mosser · 2026
Artificial intelligence (AI) and Machine Learning (ML) models are increasingly deployed in systems that, while not safety-critical per se, may still cause harm (e.g., a recent tragedy involving the death of a sixteen-year-old teenager and ChatGPT). Recent incidents have highlighted how unsafe such systems can be in practice. While safety engineering is a mature discipline for safety-critical systems, AI Safety remains closer to model validation than to safety assurance. In this paper, we propose integrating engineering practices from system safety, including argumentation models, templates, and operations, into AI Safety. This alignment enables a broader consideration of potential harm and supports the construction of explicit, structured safety arguments. We validate our approach by reframing Model Cards, a de facto industry standard, as actionable safety artifacts that make safety claims and supporting evidence explicit. We analyze state-of-the-art large language models (GPT–OSS, Claude 3, Gemma 3N) to show that it is possible to (i) identify safety templates in existing artifacts, (ii) express them as argumentation models using justification diagrams, and (iii) operationalize these models to provide immediate feedback to AI developers when evidence no longer supports safety claims.