Inclusive Artificial Intelligence
Machine Learning
2023-03-06 v2 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2212.12633,
title = {Inclusive Artificial Intelligence},
author = {Dilip Arumugam and Shi Dong and Benjamin Van Roy},
journal= {arXiv preprint arXiv:2212.12633},
year = {2023}
}