Mariia Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth, M. Vladimirova
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Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-addressed and difficult to act upon. This position paper argues that fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem: fairness findings are rarely comparable across papers or actionable for deployment decisions. This paper diagnoses recurring empirical and conceptual failure modes in current practice and motivates a shift from ad-hoc bias checks to standardized, generative-specific evaluation. We propose Fairness Cards as a minimal reporting artifact that makes evaluation choices explicit (prompt families, counterfactual protocols, metrics, and refusal handling) enabling reproducibility, comparability, and accountability. We conclude with additional recommendations towards a paradigm shift in evaluation standards. Our project page can be found at https://mariiavladimirova.github.io/fairness-cards .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2608.16974")
get_code_for_paper("2608.16974")
have("2608.16974")
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curl https://syntology.ai/api/ran/2608.16974.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2608.16974)
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