论文标题
包容性人工智能
Inclusive Artificial Intelligence
论文作者
论文摘要
评估和比较生成AIS激励响应的盛行方法，这些反应为假设的代表性个人提供了。用这些术语进行评估模型假设整个人群中的均匀偏好，并导致选择集聚的AIS，这无法代表各个个体的各种兴趣范围。我们提出了一种替代评估方法，该方法将优先考虑包容性的AIS进行了优先级，事实证明，该方法不仅保留了必要的知识，这不仅是为了随后对人口特定段的响应定制，而且还保留了公用事业最大化的决策。
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.
