Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements [Replication Package]

Artifact Summary This repository contains the replication package for the paper "Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements," presented at the IEEE International Requirements Engineering Conference 2025. Its purpose is to facilitate the verification and reproduction of the study results. The package provides an overview of all AI guidelines, AI documentation framework proposals, and model cards collected during the study, as well as the report of the subsequent thematic analysis. Paper Abstract Model cards are the primary documentation framework for developers of artificial intelligence (AI) models to communicate critical information to their users. Users are often developers themselves looking for relevant documentation to ensure that their AI implementation complies with the ethical requirements of existing laws, guidelines, and standards. However, recent studies indicate inadequate model documentation practices. This paper characterizes a gap between recent advancements in requirements engineering for AI and current practices in model documentation, aiming to provide actionable guidance to bridge this gap. We conducted a thematic analysis of 26 guidelines on ethics and AI, three AI documentation frameworks, three quantitative studies of model cards, and ten actual model cards to identify a total of 43 ethical requirements relevant to model documentation. We organize these requirements into a taxonomy featuring four themes and twelve sub-themes representing ethical principles. Our findings indicate that model developers predominantly emphasize model capabilities and reliability in the documentation while overlooking other ethical aspects, such as explainability, user autonomy, and fairness. This underscores the necessity for enhanced support in documenting ethical AI considerations. Our taxonomy serves as a foundation for a revised model card framework that holistically addresses ethical AI requirements.