TED演讲评分中的偏见检测与缓解
Detection and Mitigation of Bias in Ted Talk Ratings
摘要：
无偏见的数据汇集对于保证人工智能模型的公平性至关重要。内隐偏见，一种行为条件反射的形式，导致我们将预定的特征归因于某些群体的成员，并告知数据汇集过程。本文量化了TEDTalks（一个评估社会和职业表现的多元化社交平台）观众评分中的内隐偏见，以呈现不同类型偏见在敏感属性中的相关性。虽然这些视频的收视率应该纯粹反映扬声器的能力和技巧，我们的分析表明，存在压倒性的和占主导地位的隐性偏见，种族和性别。在我们的论文中，我们提出了检测和减轻偏见的策略，这对消除人工智能中的不公平至关重要。
英文摘要：
Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models. Implicit bias, a form of behavioral conditioning that leads us to attribute predetermined characteristics to members of certain groups and informs the data collection process. This paper quantifies implicit bias in viewer ratings of TEDTalks, a diverse social platform assessing social and professional performance, in order to present the correlations of different kinds of bias across sensitive attributes. Although the viewer ratings of these videos should purely reflect the speaker's competence and skill, our analysis of the ratings demonstrates the presence of overwhelming and predominant implicit bias with respect to race and gender. In our paper, we present strategies to detect and mitigate bias that are critical to removing unfairness in AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
