Inclusive Artificial Intelligence
This addresses the issue of AI fairness and representation for diverse user populations, though it appears incremental as it focuses on evaluation rather than a new model paradigm.
The paper tackles the problem that current generative AI evaluation methods assume homogeneous preferences, leading to models that fail to represent diverse interests, and proposes an alternative method that prioritizes inclusive AIs, which provably retain knowledge for customization and utility-maximizing decisions.
Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes homogeneous preferences across the population and engenders selection of agglomerative AIs, which fail to represent the diverse range of interests across individuals. We propose an alternative evaluation method that instead prioritizes inclusive AIs, which provably retain the requisite knowledge not only for subsequent response customization to particular segments of the population but also for utility-maximizing decisions.