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来衡量和减少几个高风险环境中的算法歧视。算法引导着越来越多的决策。与此同时，人们还担心算法决策将加剧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：通常用于定义歧视的个人决策资格通常仅适用于由现有人类或算法决策者选择进行治疗的一组个人。该项目将通过开发衡量算法歧视的新工具来克服这一基本选择挑战。该项目还将开发替代算法，以最大限度地减少或减少歧视。研究人员将把这些工具应用于多种高风险环境，包括审前拘留、就业筛选、医学检测和儿童福利调查。鉴于算法在各种情况下的迅速采用，这项研究具有相当大的政策意义。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生研究助理和博士前研究员，增加经济学研究界的多样性。这个项目产生的代码将会公开。这个研究项目将开发工具来衡量算法歧视。该项目还将开发替代的非歧视性算法，当个人的子集没有资格时。例如，在招聘方面，在面试前筛选出来的申请人在面试后是否会被雇用，并不会被观察。研究人员将表明，这种选择的挑战可以克服知识的平均合格率在不同的群体。此外，这些平均合格率可以通过利用随机分配决策者到个人来估计。这种见解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。该项目将考虑几项扩展。研究人员将利用实验来衡量算法的区别并提高准确性。算法和人类决策之间的相互作用也将被探索，因为人类的自由裁量权在大多数现实环境中仍然很重要。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。