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

点击翻译按钮获取中文摘要

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.