FairPrism Dataset

FairPrism is a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. FairPrism aims to address several limitations of existing datasets for measuring and mitigating fairness-related harms, including improved transparency, clearer specification of dataset coverage, and accounting for annotator disagreement and harms that are context-dependent. FairPrism’s annotations include the extent of stereotyping and demeaning harms, the demographic groups targeted, and appropriateness for different applications. The annotations also include specific harms that occur in interactive contexts and harms that raise normative concerns when the “speaker” is an AI system. Due to its precision and granularity, FairPrism can be used to diagnose (1) the types of fairness-related harms that AI text generation systems
cause, and (2) the potential limitations of mitigation methods.

FairPrism 是一项包含 5,000 个样本的 AI 生成英文文本数据集，该数据集拥有详细的人类标注，涵盖了与性别和性取向相关的多种伤害。FairPrism 致力于解决现有数据集在衡量和缓解与公平性相关的伤害方面的多项局限性，包括提升透明度、明确规范数据集覆盖范围，以及考虑标注者意见分歧和情境依赖性伤害。FairPrism 的标注内容涉及刻板印象和贬低性伤害的程度、被针对的群体以及适应不同应用场景的适宜性。此外，标注还包括在交互式情境中发生的具体伤害，以及当“说话者”为 AI 系统时引发的规范性争议。鉴于其精确性和细致度，FairPrism 可用于诊断 (1) AI 文本生成系统所导致的公平性相关伤害的类型，以及 (2) 缓解方法的潜在局限性。