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This paper critiques the traditional dichotomy between distributive and representational fairness in AI, arguing that generative AI's expressive nature necessitates a shift towards recognitional justice. By integrating Nancy Fraser's theory of participatory parity, the authors highlight that the core issue lies not just in misrepresentation but in misrecognition of social groups, which can perpetuate harmful stereotypes even when representations are accurate. The findings suggest that a focus on recognitional justice offers more effective frameworks for addressing fairness challenges in generative AI systems.
Misrecognition, rather than mere misrepresentation, is the critical issue in ensuring fairness in generative AI, reshaping how we approach social justice in technology.
The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally expressive: their primary function is to convey meaning rather than automate domain-specific decisions. Representational harm has also become central to value alignment, especially in research on what and whose values and perspectives AI systems should represent. Existing approaches to harms in the representation of social groups often appeal to descriptive accuracy, but this strategy has important limitations. For many social groups, no stable or bounded referent exists against which representational accuracy can be judged. It is also unclear who has the authority to decide what counts as misrepresentation, while even accurate representations can reproduce harmful social patterns. The underlying problem, we argue, is therefore not simply misrepresentation but misrecognition. Drawing on political theory, especially Nancy Fraser's account of participatory parity, we show how moving from representational fairness to recognitional justice provides better conceptual and normative tools for governing central fairness challenges in generative AI.
