A Fair Equality of Chances-based framework decomposes GenAI unfairness into harms/benefits, morally arbitrary factors, and morally decisive factors to improve measurement validity.
Troubling Taxonomies in GenAI Evaluation
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abstract
To evaluate the societal impacts of GenAI requires a model of how social harms emerge from interactions between GenAI, people, and societal structures. Yet a model is rarely explicitly defined in societal impact evaluations, or in the taxonomies of societal impacts that support them. In this provocation, we argue that societal impacts should be conceptualised as application- and context-specific, incommensurable, and shaped by questions of social power. Doing so leads us to conclude that societal impact evaluations using existing taxonomies are inherently limited, in terms of their potential to reveal how GenAI systems may interact with people when introduced into specific social contexts. We therefore propose a governance-first approach to managing societal harms attended by GenAI technologies.
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Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
A Fair Equality of Chances-based framework decomposes GenAI unfairness into harms/benefits, morally arbitrary factors, and morally decisive factors to improve measurement validity.