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Privacy, ethics, and data access: A case study of the Fragile Families Challenge

Author

Listed:
  • Ian Lundberg

    (Princeton University)

  • Arvind Narayanan

    (Princeton University)

  • Karen Levy

    (Cornell University)

  • Matthew Salganik

    (Princeton University)

Abstract

Stewards of social science data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible in order to protect the people represented in the data. In this paper, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass collaboration designed to yield insights that could improve the lives of disadvantaged children in the United States. We describe our process of threat modeling, threat mitigation, and third-party guidance.We also describe the ethical principles that formed the basis of our process. We are open about our process and the trade-offs that we made in the hopes that others can improve on what we have done.

Suggested Citation

  • Ian Lundberg & Arvind Narayanan & Karen Levy & Matthew Salganik, 2018. "Privacy, ethics, and data access: A case study of the Fragile Families Challenge," Working Papers wp18-09-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
    • Handle: RePEc:pri:crcwel:wp18-09-ff
    as

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    File URL: https://fragilefamilies.princeton.edu/sites/fragilefamilies/files/wp18-09-ff.pdf
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    References listed on IDEAS

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    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Freese, Jeremy & Peterson, David, 2017. "Replication in Social Science," SocArXiv 5bck9, Center for Open Science.
    3. Christopher Wildeman, 2009. "Parental imprisonment, the prison boom, and the concentration of childhood disadvantage," Demography, Springer;Population Association of America (PAA), vol. 46(2), pages 265-280, May.
    4. Lawrence H. Cox & Alan F. Karr & Satkartar K. Kinney, 2011. "Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act," International Statistical Review, International Statistical Institute, vol. 79(2), pages 160-183, August.
    5. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    6. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    7. Reichman, Nancy E. & Teitler, Julien O. & Garfinkel, Irwin & McLanahan, Sara S., 2001. "Fragile Families: sample and design," Children and Youth Services Review, Elsevier, vol. 23(4-5), pages 303-326.
    8. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    9. Karr, A.F. & Kohnen, C.N. & Oganian, A. & Reiter, J.P. & Sanil, A.P., 2006. "A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality," The American Statistician, American Statistical Association, vol. 60, pages 224-232, August.
    10. Chris Skinner, 2012. "Statistical Disclosure Risk: Separating Potential and Harm," International Statistical Review, International Statistical Institute, vol. 80(3), pages 349-368, December.
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    Cited by:

    1. Watts, Duncan J & Beck, Emorie D & Bienenstock, Elisa Jayne & Bowers, Jake & Frank, Aaron & Grubesic, Anthony & Hofman, Jake M. & Rohrer, Julia Marie & Salganik, Matthew, 2018. "Explanation, prediction, and causality: Three sides of the same coin?," OSF Preprints u6vz5, Center for Open Science.

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    JEL classification:

    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations

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