AI-DCL: Collaborative Research: EAGER: Understanding and Alleviating Potential Biases in Large Scale Employee Selection Systems: The Case of Automated Video Interviews

这个项目的目标是使用机器学习来理解和减轻面试官评估中的偏见。研究人员将通过研究访谈中表达行为的性别差异来做到这一点；他们会关注那些会给面试官带来不同评价的行为。更具体地说，他们将使用无人监督的视频访谈来评估在面部表情和语言风格等信号行为方面的性别差异；他们将通过研究这些差异是如何被人类采访者在他们的个性和认知能力索引中感知到的来做到这一点。在他们的研究设计中，他们依赖于大量的男性和女性受访者样本，这些受访者在通用心理能力（GMA）的标准化测试分数、自我报告的人格评分、年龄、种族和民族方面都相匹配。这项研究将为学生的跨学科培训提供新的机会，重点是招募代表性不足的群体参与这个项目。该研究将为开发无偏见的人员选择机器学习系统提供信息和指导。通过识别和解释导致机器学习选择系统中预测偏差的性别行为差异，本研究将促进我们对行为的性别表达差异、机器学习中处理偏见的方法以及人员选择和评估中的偏见减少策略的理解。本项目侧重于评估受访者属性来训练机器学习算法的两种场景：基于受访者信息（GMA考试成绩和自我报告的性格）训练的算法，以及基于观察者（采访者）评估属性训练的算法。匹配的样本确保机器学习模型的差异不是基于底层样本属性的差异。该项目的两个主要目标是：使用机器学习技术了解表达行为和采访者评级（受过训练和未受过训练的采访者）的性别差异，然后利用这种理解通过在模型中考虑这一点来减少男性和女性之间的预测差异。这些发现将产生几个重大的社会影响。它们将提高我们预测和减轻偏见的能力，为减轻机器学习中的偏见带来新的方法；并提供减少就业结果方面的社会不平等的战略和工具。这项研究也有可能推动社会科学和机器学习。它将通过揭示男性和女性表现出的行为的客观差异以及如何以不同的方式解释这些行为，提供可以推进我们对社会角色理论的理解的见解。此外，它可以推动机器学习开发新技术，以解决机器学习管道的各个阶段的偏差，从实例选择和加权，到模型拟合，再到模型选择和优化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to use machine learning to understand and mitigate bias in interviewer evaluations. The researchers will do so by examining gender differences in expressed behavior during interviews; they will focus on behaviors that can lead to different interviewer evaluations. More specifically, they will use unsupervised video interviews to assess gender differences in terms of signaling behavior, such as facial expressions and language style; they will do so by studying how these differences are perceived by human interviewers in their indexing of personality and cognitive ability. For their research design, they rely on a large sample of men and women interviewees matched on standardized test scores of General Mental Ability (GMA), self-reported personality ratings, age, race, and ethnicity. The research will provide new opportunities for interdisciplinary training of students with an emphasis on recruiting underrepresented groups to work on this project. This research will provide information and guidance for developing bias-free machine-learning systems for personnel selection. By identifying and accounting for behavioral differences between genders that lead to predictive bias in machine learning selection systems, the proposed research will advance our understanding of the differences in gender expression of behaviors, methods for dealing with bias in machine learning, and bias reduction strategies in personnel selection and assessment.This project focuses on two scenarios of assessing interviewee attributes to train machine-learning algorithms: Algorithms trained on interviewee information (GMA test scores and self-reported personality), and algorithms trained on observer (interviewer) assessment of attributes. The matched sample ensures machine-learning model differences are not based on difference in underlying sample attributes. The two main goals of the project are: To understand gender differences in expressed behaviors and interviewer ratings (trained and untrained interviewers) using machine-learning techniques, and then to use that understanding to reduce predictive discrepancies between men and women by accounting for it in the models. The findings will have several significant societal impacts. They will improve our ability to predict and mitigate biases, bring to light new methodologies for mitigating bias in machine learning; and (provide strategies and tools for reducing social inequalities in employment outcomes. This research also has potential to advance both social science and machine learning. It will provide insights that can advance our understanding of social role theory by uncovering objective differences in behavior exhibited by men and women and how these behaviors are interpreted differently. Further, it can advance machine learning in developing new techniques for addressing bias at all stages of the machine-learning pipeline from instance selection and weighting, to model fitting, and then to model selection and optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.