考虑到公平性，对视频面试进行评分
Grading video interviews with fairness considerations
摘要：
人们对使用面部图像和视频来预测人类的情感和特征产生了相当大的兴趣。最近，这项工作因不良的标签做法、不确定的预测结果和公平性考虑而受到批评。我们提出了一个仔细的方法来自动获得社交技能的候选人的基础上，他们的视频回答面试问题。我们第一次包括来自多个国家、多个种族的视频数据。此外，来自多个种族背景的个人根据几种最佳实践对这些视频进行了评分，以实现对社交技能的共识和公正的衡量。我们开发了两个机器学习模型来预测社交技能。第一个模型采用专家指导，使用合理的因果关系的功能。第二种方法使用深度学习，仅依赖于数据中存在的经验相关性。我们比较了这两种模型的误差，研究了模型的特异性，并提出了建议。我们通过研究种族和性别模型的错误来进一步分析公平性。我们通过确定模型对候选人面试结果的预测程度来验证模型的有用性。总的来说，该研究为使用人工智能进行视频面试评分提供了强有力的支持，同时兼顾了公平性和道德方面的考虑。
英文摘要：
There has been considerable interest in predicting human emotions and traits using facial images and videos. Lately, such work has come under criticism for poor labeling practices, inconclusive prediction results and fairness considerations. We present a careful methodology to automatically derive social skills of candidates based on their video response to interview questions. We, for the first time, include video data from multiple countries encompassing multiple ethnicities. Also, the videos were rated by individuals from multiple racial backgrounds, following several best practices, to achieve a consensus and unbiased measure of social skills. We develop two machine-learning models to predict social skills. The first model employs expert-guidance to use plausibly causal features. The second uses deep learning and depends solely on the empirical correlations present in the data. We compare errors of both these models, study the specificity of the models and make recommendations. We further analyze fairness by studying the errors of models by race and gender. We verify the usefulness of our models by determining how well they predict interview outcomes for candidates. Overall, the study provides strong support for using artificial intelligence for video interview scoring, while taking care of fairness and ethical considerations.
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。
