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Data Science for Social Good and Public Policy: Examples, Opportunities, and Challenges

Published: 27 June 2018 Publication History

Abstract

Can data science help reduce police violence and misconduct? Can it help increase retention of patients in care? Can it help prevent children from getting lead poisoning? Can it help cities better target limited resources to improve lives of citizens? We're all aware of the hype around data science and related buzzwords right now but turning this hype into social impact takes cross-disciplinary training, teams, and methods. In this talk, I'll discuss lessons learned from our work at University of Chicago while working on dozens of data science projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. I'll highlight opportunities for IR researchers to get involved in these efforts as well as information retrieval challenges that are open research problems that need to be solved in order to increase the effectiveness of today's machine learning and data science algorithms in order to have social and policy impact in a fair and equitable manner.

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  1. Data Science for Social Good and Public Policy: Examples, Opportunities, and Challenges

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      cover image ACM Conferences
      SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
      June 2018
      1509 pages
      ISBN:9781450356572
      DOI:10.1145/3209978
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 27 June 2018

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      Author Tags

      1. data science
      2. equitable computing

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      • Keynote

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      SIGIR '18
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      Acceptance Rates

      SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      • (2024)Understanding and mitigating the impact of race with adversarial autoencodersCommunications Medicine10.1038/s43856-024-00627-34:1Online publication date: 23-Oct-2024
      • (2023)Forecasting carbon emissions future prices using the machine learning methodsAnnals of Operations Research10.1007/s10479-023-05188-7Online publication date: 6-Feb-2023
      • (2022)Forecasting for social goodInternational Journal of Forecasting10.1016/j.ijforecast.2021.02.01038:3(1245-1257)Online publication date: Jul-2022
      • (2022)Computational Models for Social Good: Beyond Bias and RepresentationSocial, Cultural, and Behavioral Modeling10.1007/978-3-031-17114-7_25(263-267)Online publication date: 18-Sep-2022
      • (2022)Forecasting and its BeneficiariesThe Palgrave Handbook of Operations Research10.1007/978-3-030-96935-6_21(695-717)Online publication date: 8-Jul-2022

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