公众演讲言语和手势质量意识对评分预测的公平性
Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches
摘要：
几十年来，语言和非语言线索在伟大的公开演讲中的作用一直是一个探索的话题。我们确定了现有理论的共性，即沟通渠道或模式中的“多样性或异质性”元素（例如诉诸故事，科学事实，情感联系，面部表情等）。这对于有效地传递信息是必不可少的。我们使用这个观察正式一个新的异质性度量，HEM，量化的谈话质量在口头和非口头域（成绩单和面部表情）。我们使用TED演讲作为公开演讲的输入库，因为它由来自不同社区的演讲者组成，除了具有广泛的外展性。我们发现，有一个有趣的关系HEM和观众给演讲者的TED演讲的评级。它强调HEM内在地并且成功地代表了基于“多样性或异质性”的谈话质量。此外，我们还发现，HEM成功地捕捉到了关于种族和性别的普遍偏见，我们称之为敏感属性（因为基于这些属性的预测可能会导致不公平的结果）。我们将HEM度量纳入神经网络的损失函数中，目标是减少种族和性别方面的评级预测中的不公平性。我们的研究结果表明，修改后的损失函数提高了预测的公平性，而不会显着影响神经网络的预测精度。我们的工作结合在一起的一个新的度量公共演讲在口头和非口头域的计算能力的神经网络设计一个公平的预测系统的发言者。
英文摘要：
The role of verbal and non-verbal cues towards great public speaking has been a topic of exploration for many decades. We identify a commonality across present theories, the element of "variety or heterogeneity" in channels or modes of communication (e.g. resorting to stories, scientific facts, emotional connections, facial expressions etc.) which is essential for effectively communicating information. We use this observation to formalize a novel HEterogeneity Metric, HEM, that quantifies the quality of a talk both in the verbal and non-verbal domain (transcript and facial gestures). We use TED talks as an input repository of public speeches because it consists of speakers from a diverse community besides having a wide outreach. We show that there is an interesting relationship between HEM and the ratings of TED talks given to speakers by viewers. It emphasizes that HEM inherently and successfully represents the quality of a talk based on "variety or heterogeneity". Further, we also discover that HEM successfully captures the prevalent bias in ratings with respect to race and gender, that we call sensitive attributes (because prediction based on these might result in unfair outcome). We incorporate the HEM metric into the loss function of a neural network with the goal to reduce unfairness in rating predictions with respect to race and gender. Our results show that the modified loss function improves fairness in prediction without considerably affecting prediction accuracy of the neural network. Our work ties together a novel metric for public speeches in both verbal and non-verbal domain with the computational power of a neural network to design a fair prediction system for speakers.
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