{"id":841198,"date":"2022-05-02T07:50:04","date_gmt":"2022-05-02T14:50:04","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=841198"},"modified":"2022-07-29T11:29:30","modified_gmt":"2022-07-29T18:29:30","slug":"designing-an-ai-driven-neighborhood-navigator-with-black-and-latinx-nyc-residents","status":"publish","type":"msr-video","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/video\/designing-an-ai-driven-neighborhood-navigator-with-black-and-latinx-nyc-residents\/","title":{"rendered":"Designing an AI-driven Neighborhood Navigator with Black and Latinx NYC Residents"},"content":{"rendered":"

An interdisciplinary team of social scientists, computer scientist, designers, and researchers from the SAFElab at Columbia University\u2019s School of Social Work, School of Engineering and Applied Science and Data Science Institute partnered with the Research and Evaluation Center (REC) at John Jay College of Criminal Justice to develop a Neighborhood Navigator which assesses patterns and changes in the sentiment of quality of life, wellbeing, community, and living conditions among residents of New York City.\u00a0The Neighborhood Navigator uses community focus groups and one-on-one interviews in concert with artificial intelligence (AI) techniques (e.g. natural language processing (NLP) and computer vision) to provide short-term, recurring feedback on resident sentiment. Over time, greater precision in the AI components could lead to reduced dependence on surveys and more cost-efficient sustainability. The tool will provide policymakers with insight into public sentiment about government work and allow them to respond accordingly.<\/p>\n

Learning Materials<\/h3>\n

By and featuring Dr. Patton<\/strong><\/p>\n