Measuring and Reducing Algorithmic Discrimination with Quasi-Experimental Data

该研究项目将开发新的工具来测量和减少几个高风险环境中的算法歧视。算法指导越来越多的决策。与此同时，人们担心算法决策将加深或加剧对受法律保护群体的歧视。然而，量化算法歧视往往受到选择挑战的阻碍：一个人的决策资格，通常用于定义歧视，通常只适用于由现有人类或算法决策者选择治疗的一组人。该项目将通过开发新的工具来衡量算法歧视来克服这一基本的选择挑战。该项目还将开发替代算法，最大限度地减少或减少歧视。研究人员将在多个高风险环境中应用这些工具，包括审前拘留，就业筛选，医疗测试和儿童福利调查。这项研究是相当大的政策利益，快速采用的算法在各种设置。研究人员致力于通过招募，培训和指导女性，代表性不足的少数民族和第一代大学生作为本科研究助理和博士前研究员来增加经济学研究社区的多样性。该项目产生的代码将公开提供。该研究项目将开发测量算法歧视的工具。该项目还将开发替代的非歧视性算法时，资格是未观察到的一个子集的个人。例如，在就业方面，对于在面试之前被筛选出来的申请人，不观察一个人在面试之后是否会被雇用。研究人员将表明，这种选择挑战可以通过了解不同群体的平均合格率来克服。此外，这些平均合格率可以通过利用决策者对个人的随机分配来估计。这种洞察力不仅可以用来衡量算法歧视，还可以用来开发减少或消除歧视的替代算法。该项目将考虑几个扩展。研究人员将利用实验来测量算法的区分度并提高准确性。算法和人类决策之间的相互作用也将被探讨，因为人类的判断力在大多数现实世界中仍然很重要。这项研究的结果将对更准确地量化算法透明度，准确性和公平性之间的权衡产生影响。该奖项反映了NSF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。

This research project will develop new tools to measure and reduce algorithmic discrimination in several high-stakes settings. Algorithms guide an increasingly large number of decisions. Alongside this rise is a concern that algorithmic decision-making will entrench or worsen discrimination against legally protected groups. However, quantifying algorithmic discrimination is often hampered by a selection challenge: an individual's qualification for a decision, which is often used to define discrimination, is typically only available for the group of individuals who were selected for treatment by an existing human or algorithmic decision-maker. This project will overcome this fundamental selection challenge by developing new tools to measure algorithmic discrimination. The project also will develop alternative algorithms that minimize or reduce discrimination. The researchers will apply these tools in multiple high-stakes settings, including pretrial detention, employment screening, medical testing, and child welfare investigations. The research is of considerable policy interest given the rapid adoption of algorithms in a variety of settings. The investigators are committed to increasing diversity in the economics research community by recruiting, training, and mentoring women, under-represented minorities, and first-generation college students as undergraduate research assistants and predoctoral fellows. Code produced by this project will be made publicly available.This research project will develop tools to measure algorithmic discrimination. The project also will develop alternative non-discriminatory algorithms when qualification is unobserved for a subset of individuals. For example, in the employment context, whether an individual would be hired after an interview is not observed for applicants screened out before the interview is held. The investigators will show that this selection challenge can be overcome with knowledge of average qualification rates across different groups. Further, these average qualification rates can be estimated by utilizing random assignment of decision-makers to individuals. This insight can be used not only to measure algorithmic discrimination, but to develop alternative algorithms that reduce or eliminate discrimination. The project will consider several extensions. The investigators will utilize experimentation to measure algorithmic discrimination and improve accuracy. The interaction between algorithms and human decision-making also will be explored, as human discretion remains important in most real-world settings. The results of this research will have implications for more accurately quantifying the trade-offs between algorithmic transparency, accuracy, and fairness.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.