Measuring and Reducing Algorithmic Discrimination with Quasi-Experimental Data

用准实验数据测量和减少算法歧视

• 批准号: 2119849
• 负责人: Will Dobbie
• 金额: $ 40万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119849&HistoricalAwards=false
• 关键词: Measuring; Reducing; Algorithmic; Discrimination; Quasi

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.

该研究项目将开发新的工具，以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定。与此同时，算法决策将巩固或恶化对受合法保护的群体的歧视。但是，量化算法歧视通常会受到选择挑战的阻碍：个人的决策资格通常用于定义歧视，通常仅适用于被现有的人类或算法决策者选择接受治疗的个人组。该项目将通过开发新工具来衡量算法歧视，克服这一基本选择挑战。该项目还将开发替代算法，以最大程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，就业筛查，医疗测试和儿童福利调查。鉴于在各种环境中迅速采用算法，这项研究具有相当大的政策利益。调查人员致力于通过招募，培训和指导妇女，代表性不足的少数民族以及第一代大学生作为本科研究助理和专业研究员来增加经济学研究界的多样性。该项目生产的代码将被公开可用。本研究项目将开发用于衡量算法歧视的工具。当对个人的子集未观察到资格时，该项目还将开发替代性的非歧视算法。例如，在就业背景下，在举行面试之前未对申请人进行面试后是否会雇用个人。研究人员将表明，通过了解不同群体的平均资格率的知识，可以克服这一选择挑战。此外，可以通过将决策者随机分配给个人来估算这些平均资格率。该见解不仅可以用来衡量算法歧视，还可以用于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歧视并提高准确性。还将探讨算法与人类决策之间的相互作用，因为在大多数现实世界中，人类酌处权仍然很重要。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具有影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。