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

这项学院早期职业发展计划(Career)赠款将通过开发系统的方法来减少由于隐性偏见而导致的劳动力不平等，从而促进国家健康、繁荣和福利。广泛使用的自动申请者筛选技术带来了系统性筛选明星(通过替代途径培训的熟练工人)的高风险，而对来自不同背景的候选人的选择实施配额可能违反反歧视法。该奖项支持制定基本方法，透明地处理候选人评价数据中的背景偏见，而不诉诸配额或公平限制(由于法律要求)。这种跨学科的研究将为从业者和政策制定者提供工具，以了解系统中的低效，导致招聘和大学招生政策的协同设计。随附的教育计划旨在为高中生开发STEAM(STEM+ART)工作坊，通过“招聘经理”模拟游戏、面向政策和法律专业人员的工作坊、道德或运筹学课程的设计以及对学生的持续指导，重点关注STEM少数群体，为高中生理解数据中的偏差。本研究将开发基本的方法来模拟数据的变异性，通过使用反事实和因果分析来构建考生评估数据的基数和序数可变性集合，分别产生双线性优化问题和序数组合优化问题。这项研究将开发新的技术来解决这些具有挑战性的问题类，以参数优化和排序理论为起点，并找到易于处理的解决方案。这项工作将从根本上改变我们处理上下文数据的方式，同时推进顺序、稳健、参数和一般离散优化理论。这项工作将解决与公平和效率权衡相关的重要政策设计问题，例如，当数据是背景数据时，偏见感知技术对公平、多样性和公平性的影响，在可变性集合的构建中改变“风险”参数对候选人选择的影响，并突出如何利用有限的资源来减少可变性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.