CAREER: Advancing Equity in Selection Problems Through Bias-Aware Optimization

职业：通过偏差感知优化促进选择问题的公平性

• 批准号: 2239824
• 负责人: Swati Gupta
• 金额: $ 53.19万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239824&HistoricalAwards=false
• 关键词: CAREER; Advancing; Equity; Selection; Problems

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national health, prosperity, and welfare by developing systematic approaches to reducing workforce inequality due to implicit bias. Widely used automated applicant screening technologies bring high risk of systematically screening-out STARS (skilled workers trained by alternate routes), while imposing quotas on the selection of candidates from different backgrounds can violate anti-discrimination laws. This award supports the development of fundamental methodologies for transparently handling contextual biases in candidate evaluation data without resorting to quotas or fairness constraints (due to legal requirements). This interdisciplinary research will provide tools for practitioners and policymakers to understand the inefficiencies in the system, leading to a synergistic design of policies for hiring and college admissions. The accompanying educational plan aims to develop STEAM (STEM+art) workshops for high school students to understand biases in data through a “hiring manager” simulation game, a workshop geared towards policy and law professionals, the design of courses in Ethical OR, and the continued mentorship of students with a focus on STEM minorities.This research will develop fundamental methodologies to model variability in data due to its context, by using counterfactual and causal analysis to construct cardinal and ordinal variability sets for candidates’ evaluation data, yielding bilinear optimization problems and ordinal combinatorial optimization respectively. The research will develop new techniques to address these challenging problem classes using parametric optimization and order theory as a start and find tractable solutions. This work will provide a fundamental shift in how we process contextual data, while advancing the theories of ordinal, robust, parametric, and general discrete optimization. The work will address important policy-design questions related to equity-efficiency trade-offs, e.g., the impact of bias-aware techniques on equity, diversity, and fairness when data is contextual, the impact of changing "risk" parameters in the construction of variability sets on candidate selection and highlight ways to use limited resources to reduce variability.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.

这项教师的早期职业发展计划（职业）赠款将通过开发系统的系统方法来减少由于隐性偏见而减少劳动力不平等的方法来促进国家健康，繁荣和福利的发展。广泛使用的自动化适用筛查技术带来了系统筛选星星（通过替代路线训练的熟练工人）的高风险，同时对来自不同背景的候选人的选择强加了配额可以违反反歧视法。该奖项支持开发基本方法，用于透明地处理候选评估数据中的上下文偏见，而无需诉诸配额或公平限制（由于法律要求）。这项跨学科的研究将为从业者和政策制定者提供工具，以了解系统中效率低下的效率，从而导致招聘和大学入学政策的协同设计。 The participating educational plan aims to develop STEAM (STEM+art) workshops for high school students to understand biases in data through a “hiring manager” simulation game, a workshop geared towards policy and law professionals, the design of courses in Ethical OR, and the continued mentality of students with a focus on STEM minorities.This research will develop fundamental methods to model variability in data due to its context, by using counterfactual and causal analysis to construct cardinal and候选人评估数据的序数变异性集，分别产生双线性优化问题和顺序组合优化。该研究将开发新技术，以参数优化和秩序理论作为开始并找到可拖动的解决方案来解决这些挑战问题类别。这项工作将提供我们处理上下文数据的方式的根本转变，同时推进序数，健壮，参数和一般离散优化的理论。这项工作将解决与公平效率取舍有关的重要政策设计问题，例如，当数据是上下文时，偏见意识到的技术对公平，多样性和公平性的影响，在构建变异性方面改变“风险”参数的影响，在变异性构建中的“风险”参数对候选人选择的构建对候选资源的构建以及通过降低差异的质疑，并高高地位的奖励的奖励。智力优点和更广泛的影响审查标准。