E-Mail A team of researchers at New York University will develop new methods and tools aimed at minimizing systemic biases and producing more equitable public policy impacts on such areas as city housing inspections, policing, and courts. Under a $1 million grant from the National Science Foundation (NSF) and Amazon, Computer Science Professor Daniel B. Neill will lead the three-year research project centered on the growing use of Artificial Intelligence (AI) by urban public sector organizations--work that will include the creation of open source tools for assessing and correcting biases. "Human decisions and algorithmic decisions have potential for systematic biases that may lead to poor downstream outcomes such as disparities and inequity across racial, gender, and socioeconomic lines," said Neill, a professor at NYU's Wagner Graduate School of Public Service and the Center for Urban Science and Progress (CUSP) at NYU's Tandon School of Engineering. "What we want to understand is how algorithms can enhance human decision making by eliminating implicit biases, and to develop methods and tools to assist those designing and implementing policy interventions in cities."