FW-HTF-P: Combining Human and Machine Intelligence to Improve Equity and Fairness in the Work of Municipal, Public Sector Decision Making

FW-HTF-P：结合人类和机器智能，提高市政、公共部门决策工作的公平性和公平性

• 批准号: 2129038
• 负责人: Motahhare Eslami
• 金额: $ 15万
• 依托单位: Carnegie-Mellon 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: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129038&HistoricalAwards=false
• 关键词: FW; HTF; Combining; Human; Machine

Government agencies and the public sector have increasingly turned to data-driven and Artificial Intelligence (AI)-based techniques to enhance the quality of human decision-making and service provision. This shift toward an AI-driven approach holds promise in its ability to offer much-needed support to understaffed departments and increasing the speed with which community members’ concerns are met. However, an over-emphasis on efficiency and effectiveness, along with most AI-based systems’ lack of transparency into how a given recommendation is made, have made it difficult for public sector workers to make fair, equitable, efficient decisions. This can result in lack of accountability to constituents and cause harm to minoritized communities, as demonstrated by cases such as racial bias in predictive policing systems. This project will use a service design-inspired approach to investigate how public sector data and algorithmic infrastructures are formed, factors that can lead to disparate municipal service provision, and how these infrastructures might be redesigned to address the inequities they sometimes reinforce. This planning grant will give the project team additional insight into the problem while helping the team build community partnerships and expand its disciplinary expertise, in order to develop a larger, long-term research agenda. The project involves four major activities, which will be conducted in the context of business owners’ interaction with public sector permit, license, and inspection departments that use or plan to use data-innovation and AI-enhanced decision-making tools. The first activity involves characterizing the current state of data collected and analyzed using a data audit methodology, with the goal of identifying needed changes to disaggregate business owners by qualities related to equity and fairness and to support business process mining. The second and third activities are to understand the priorities and experiences of public sector workers who make decisions about permits, licenses, and inspections, and the experience and challenges faced by local, small, women- and minority-owned businesses that need these services in order to launch their ventures. Those activities will be based on interviews with members of the respective populations, culminating in a fourth activity in which these stakeholder groups will come together with the research team in co-design workshops to help define and balance the key objectives held by all stakeholders, along with ideas for how systems can be shaped to achieve those objectives in AI-based systems that support public sector decision making.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.

政府机构和公共部门越来越多地转向数据驱动和基于人工智能 (AI) 的技术，以提高人类决策和服务提供的质量。这种向人工智能驱动方法的转变为其提供服务的能力带来了希望。为人手不足的部门提供急需的支持，并提高满足社区成员关切的速度。然而，过分强调效率和有效性，以及大多数基于人工智能的系统在如何提出特定建议方面缺乏透明度，使公共部门工作人员难以做出这可能会导致缺乏对选民的问责，并对少数群体造成伤害，正如预测性警务系统中的种族偏见等案例所证明的那样，该项目将使用受服务设计启发的方法来调查公共行为。部门数据和算法基础设施的形成、可能导致不同市政服务提供的因素，以及如何重新设计这些基础设施以解决它们有时加剧的不平等问题。这笔规划拨款将使项目团队在帮助团队的同时，对问题有更多的了解。建立社区伙伴关系并扩大其该项目涉及四项主要活动，这些活动将在企业主与使用或计划使用的公共部门许可、执照和检查部门互动的背景下进行。使用数据创新和人工智能增强的决策工具第一项活动涉及使用数据审计方法来描述收集和分析的数据的当前状态，目的是确定所需的更改，以按与公平和公平相关的质量对企业主进行分类。并支持业务流程挖掘。第三项活动是了解公共部门工作人员在许可证、执照和检查方面做出决定的优先事项和经验，以及需要这些服务才能启动的当地、小型、妇女和少数族裔企业所面临的经验和挑战这些活动将基于对各自人群成员的访谈，最终形成第四项活动，这些利益相关者团体将与研究团队一起参加共同设计研讨会，以帮助确定和平衡所有人持有的关键目标。利益相关者，以及如何塑造系统的想法在支持公共部门决策的基于人工智能的系统中实现这些目标。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。