1932

Abstract

The rise of big data and machine learning is a polarizing force among those studying inequality and the law. Big data and tools like predictive modeling may amplify inequalities in the law, subjecting vulnerable individuals to enhanced surveillance. But these data and tools may also serve an opposite function, shining a spotlight on inequality and subjecting powerful institutions to enhanced oversight. We begin with a typology of the role of big data in inequality and the law. The typology asks questions—Which type of individual or institutional actor holds the data? What problem is the actor trying to use the data to solve?—that help situate the use of big data within existing scholarship on law and inequality. We then highlight the dual uses of big data and computational methods—data for surveillance and data as a spotlight—in three areas of law: rental housing, child welfare, and opioid prescribing. Our review highlights asymmetries where the lack of data infrastructure to measure basic facts about inequality within the law has impeded the spotlight function.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-lawsocsci-061020-050543
2020-10-13
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/lawsocsci/16/1/annurev-lawsocsci-061020-050543.html?itemId=/content/journals/10.1146/annurev-lawsocsci-061020-050543&mimeType=html&fmt=ahah

Literature Cited

  1. Albiston C. 1999. The rule of law and the litigation process: the paradox of losing by winning. Law Soc. Rev. 33:4869–910
    [Google Scholar]
  2. Albiston CR, Sandefur RL. 2013. Expanding the empirical study of access to justice. Wis. Law Rev. 2013:101–20
    [Google Scholar]
  3. Allen JA. 2019. The color of algorithms: an analysis and proposed research agenda for deterring algorithmic redlining. Fordham Urban Law J 46:219–70
    [Google Scholar]
  4. Aronowitz M, Golding E. 2019. HUD's proposal to revise the disparate impact standard will impede efforts to close the homeownership gap Publ., Hous. Financ. Policy Cent., Urban Inst. Washington, DC: https://www.urban.org/sites/default/files/publication/101015/huds_proposal_to_revise_the_disparate_impact_standard_0.pdf
  5. Barocas S, Selbst AD. 2016. Big data's disparate impact. Calif. Law Rev. 104:671–732
    [Google Scholar]
  6. Berk R, Heidari H, Jabbari S, Kearns M, Roth A 2018. Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. https://doi.org/10.1177/0049124118782533
    [Crossref] [Google Scholar]
  7. Black RC, Hall ME, Owens RJ, Ringsmuth EM 2016a. The role of emotional language in briefs before the US Supreme Court. J. Law Courts 4:377–407
    [Google Scholar]
  8. Black RC, Owens RJ, Wedeking J, Wohlfarth PC 2016b. The influence of public sentiment on Supreme Court opinion clarity. Law Soc. Rev. 50:703–32
    [Google Scholar]
  9. Black RC, Spriggs JF. 2013. The citation and depreciation of US Supreme Court precedent. J. Empir. Legal Stud. 10:325–58
    [Google Scholar]
  10. Bommarito MJ II, Katz D, Zelner J 2009. Law as a seamless web? Comparison of various network representations of the United States Supreme Court corpus (1791–2005). Proceedings of the 12th International Conference on Artificial Intelligence and Law234–35 New York: Assoc. Comput. Mach.
    [Google Scholar]
  11. Brauneis R, Goodman EP. 2018. Algorithmic transparency for the smart city. Yale J. Law Technol. 20:103–76
    [Google Scholar]
  12. Brayne S. 2017. Big data surveillance: the case of policing. Am. Sociol. Rev. 82:977–1008
    [Google Scholar]
  13. Brayne S. 2018. The criminal law and law enforcement implications of big data. Annu. Rev. Law Soc. Sci. 14:293–308
    [Google Scholar]
  14. Brick JM, Tourangeau R. 2017. Responsive survey designs for reducing nonresponse bias. J. Off. Stat. 33:735–52
    [Google Scholar]
  15. Brown A, Chouldechova A, Putnam-Hornstein E, Tobin A, Vaithianathan R 2019. Toward algorithmic accountability in public services: a qualitative study of affected community perspectives on algorithmic decision-making in child welfare services. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems1–12 New York: Assoc. Comput. Mach.
    [Google Scholar]
  16. Carlson K, Livermore MA, Rockmore D 2015. A quantitative analysis of writing style on the U.S. Supreme Court. Wash. Univ. Law Rev. 93:1461–510
    [Google Scholar]
  17. Carton S, Helsby J, Joseph K, Mahmud A, Park Y et al. 2016. Identifying police officers at risk of adverse events. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining67–76 New York: Assoc. Comput. Mach.
    [Google Scholar]
  18. Chen DL, Ash E. 2020. Case vectors: spatial representations of the law using document embeddings. Comput. Anal. Law. In press
    [Google Scholar]
  19. Chen MK, Haggag K, Pope DG, Rohla R 2019. Racial disparities in voting wait times: evidence from smartphone data Work. Pap. 26487 Natl. Bur. Econ. Res Cambridge, MA:
  20. Chien CV, Sukhatme NE. 2019. A proposal for policypilots.gov. Regulatory Review Nov. 19. https://www.theregreview.org/2019/11/19/chien-sukhatme-proposal-policypilots-gov/
    [Google Scholar]
  21. Chouldechova A. 2017. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5:153–63
    [Google Scholar]
  22. Collatz A. 2019. SmartMove's ResidentScore vs. a typical credit score: Which is better?. SmartMove April 24. https://www.mysmartmove.com/SmartMove/blog/residentscore-tailored-tenant-screening.page
    [Google Scholar]
  23. Crawford K, Schultz J. 2014. Big data and due process: toward a framework to redress predictive privacy harms. Boston Coll. Law Rev. 55:93–128
    [Google Scholar]
  24. Desmond M. 2016. Evicted: Poverty and Profit in the American City New York: Broadway Books
  25. Desmond M, Bell M. 2015. Housing, poverty, and the law. Annu. Rev. Law Soc. Sci. 11:15–35
    [Google Scholar]
  26. Desmond M, Gromis A, Edmonds L, Hendrickson J, Krywokulski K et al. 2018. Eviction Lab Methodology Report: Version 1.0 Princeton, NJ: Princeton Univ. Press
  27. Dettlaff AJ, Rivaux SL, Baumann DJ, Fluke JD, Rycraft JR, James J 2011. Disentangling substantiation: the influence of race, income, and risk on the substantiation decision in child welfare. Child. Youth Serv. Rev. 33:1630–37
    [Google Scholar]
  28. Dobbin F. 2009. Inventing Equal Opportunity Princeton, NJ: Princeton Univ. Press
  29. Dunn E, Grabchuk M. 2010. Background checks and social effects: contemporary residential tenant-screening problems in Washington State. Seattle J. Soc. Just. 9:319–99
    [Google Scholar]
  30. Edelman LB. 1992. Legal ambiguity and symbolic structures: organizational mediation of civil rights law. Am. J. Sociol. 97:1531–76
    [Google Scholar]
  31. Eubanks V. 2018a. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor New York: St. Martin's
  32. Eubanks V. 2018b. A child abuse prediction model fails poor families. Wired Jan. 15. https://www.wired.com/story/excerpt-from-automating-inequality/
    [Google Scholar]
  33. Felstiner WLF, Abel RL, Sarat A 1980. The emergence and transformation of disputes: naming, blaming, claiming. Law Soc. Rev. 15:631–54
    [Google Scholar]
  34. Ferguson AG. 2016. Policing predictive policing. Wash. Univ. Law Rev. 94:1109–89
    [Google Scholar]
  35. Ferguson AG. 2019. The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement New York: N.Y. Univ. Press
  36. Font SA, Berger LM, Cancian M, Noyes JL 2018. Permanency and the educational and economic attainment of former foster children in early adulthood. Am. Sociol. Rev. 83:716–43
    [Google Scholar]
  37. Foster I, Ghani R, Jarmin RS, Kreuter F, Lane J 2016. Big Data and Social Science: A Practical Guide to Methods and Tools Boca Raton, FL: CRC
  38. Frankenreiter J, Livermore M. 2020. Computational methods in legal analysis. Annu. Rev. Law Soc. Sci. 16:3957
    [Google Scholar]
  39. Fuller SR, Edelman LB, Matusik SF 2000. Legal readings: employee interpretation and mobilization of law. Acad. Manag. Rev. 25:200–16
    [Google Scholar]
  40. Galanter M. 1974. Why the “haves” come out ahead: speculations on the limits of legal change. Law Soc. Rev. 9:95–160
    [Google Scholar]
  41. Gluck AR, Hall A, Curfman G 2018. Civil litigation and the opioid epidemic: the role of courts in a national health crisis. J. Law Med. Ethics 46:351–66
    [Google Scholar]
  42. Greiner DJ, Matthews A. 2016. Randomized control trials in the United States legal profession. Annu. Rev. Law Soc. Sci. 12:295–312
    [Google Scholar]
  43. Greiner DJ, Pattanayak CW. 2011. Randomized evaluation in legal assistance: What difference does representation (offer and actual use) make. Yale Law J 121:2118–214
    [Google Scholar]
  44. Greiner DJ, Pattanayak CW, Hennessy J 2012. The limits of unbundled legal assistance: a randomized study in a Massachusetts district court and prospects for the future. Harvard Law Rev 126:901–89
    [Google Scholar]
  45. Hampton RL, Newberger EH. 1985. Child abuse incidence and reporting by hospitals: significance of severity, class, and race. Am. J. Public Health 75:56–60
    [Google Scholar]
  46. Harron K, Dibben C, Boyd J, Hjern A, Azimaee M et al. 2017. Challenges in administrative data linkage for research. Big Data Soc 4: https://doi.org/10.1177/2053951717745678
    [Crossref] [Google Scholar]
  47. Heimer CA, Staffen LR. 1995. Interdependence and reintegrative social control: labeling and reforming “inappropriate” parents in neonatal intensive care units. Am. Sociol. Rev. 60:635–54
    [Google Scholar]
  48. Helsby J, Carton S, Joseph K, Mahmud A, Park Y et al. 2018. Early intervention systems: predicting adverse interactions between police and the public. Crim. Justice Policy Rev. 29:190–209
    [Google Scholar]
  49. Hepburn P, Faber J, Kneebone E, Hendrickson J, Thomas T et al. 2019. Super session: causes and consequences of eviction Presented at the 41st Annual Fall Research Conference, Association for Public Policy Analysis and Management Denver, CO: Nov 7–9
  50. Humphries JE, Mader NS, Tannenbaum DI, Van Dijk WL 2019. Does eviction cause poverty? Quasi-experimental evidence from Cook County, IL Work. Pap. 26139 Natl. Bur. Econ. Res. Washington, DC:
  51. Kalev A, Dobbin F. 2006. Enforcement of civil rights law in private workplaces: the effects of compliance reviews and lawsuits over time. Law Soc. Inq. 31:855–903
    [Google Scholar]
  52. Kleysteuber R. 2006. Tenant screening thirty years later: a statutory proposal to protect public records. Yale Law J 116:1344–88
    [Google Scholar]
  53. Knox D, Lucas C. 2019. A dynamic model of speech for the social sciences Work. Pap Princeton Univ. Princeton, NJ: https://asiapolmeth.princeton.edu/sites/default/files/polmeth/files/deanknox.pdf
  54. Lazer D, Pentland A, Adamic L, Aral S, Barabási AL et al. 2009. Computational social science. Science 323:721–23
    [Google Scholar]
  55. Lazer D, Radford J. 2017. Data ex machina: introduction to big data. Annu. Rev. Sociol. 43:19–39
    [Google Scholar]
  56. Lebovits G, Addonizio JM. 2015. The use of tenant screening reports and tenant blacklisting LEGALEase Pamphlet, N.Y. State Bar Assoc Albany, NY:
  57. Legal Aid Society 2020. Cop Accountability Project (Cap) https://legalaidnyc.org/programs-projects-units/the-cop-accountability-project/
  58. Lippincott T, Carrell A. 2018. Observational comparison of geo-tagged and randomly-drawn tweets. Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media M Nissim, V Patti, B Plank 50–55 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  59. Massey DS. 2015. The legacy of the 1968 Fair Housing Act. Sociol. Forum 30:571–88
    [Google Scholar]
  60. McCourt School of Public Policy 2020. McCourt students use data to improve housing inspections in DC. News Jan. 13. https://mccourt.georgetown.edu/news/using-data-to-improve-housing-inspections-in-dc/
    [Google Scholar]
  61. Metcalf GR. 1988. Fair Housing Comes of Age Contrib. Political Sci. 198 Santa Barbara, CA: Praeger
  62. Mooney SJ, Westreich DJ, El-Sayed AM 2015. Epidemiology in the era of big data. Epidemiology 26:390–94
    [Google Scholar]
  63. Munger FW, Seron C. 2017. Race, law, and inequality, 50 years after the Civil Rights era. Annu. Rev. Law Soc. Sci. 13:331–50
    [Google Scholar]
  64. O'Neil C. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy New York: Crown
  65. Ouellet M, Hashimi S, Gravel J, Papachristos AV 2019. Network exposure and excessive use of force: investigating the social transmission of police misconduct. Criminol. Public Policy 18:675–704
    [Google Scholar]
  66. Pasquale F. 2015. The Black Box Society: The Secret Algorithms that Control Money and Information Cambridge, MA: Harvard Univ. Press
  67. Pettigrew S. 2017. The racial gap in wait times: why minority precincts are underserved by local election officials. Political Sci. Q. 132:527–47
    [Google Scholar]
  68. Prescott JJ, Starr SB. 2020. Expungement of criminal convictions: an empirical study. Harvard Law Rev 133:2460–555
    [Google Scholar]
  69. Putnam-Hornstein E, Prindle JJ, Leventhal JM 2016. Prenatal substance exposure and reporting of child maltreatment by race and ethnicity. Pediatrics 138:e20161273
    [Google Scholar]
  70. Raghavan M, Barocas S, Kleinberg J, Levy K 2019. Mitigating bias in algorithmic employment screening: Evaluating claims and practices. arXiv:1906.09208 [cs.CY]
  71. Randall SM, Ferrante AM, Boyd JH, Semmens JB 2013. The effect of data cleaning on record linkage quality. BMC Med. Inform. Decis. Making 13:64
    [Google Scholar]
  72. Richardson R, Schultz J, Crawford K 2019. Dirty data, bad predictions: how civil rights violations impact police data, predictive policing systems, and justice. N.Y. Univ. Law Rev. Online 94:192–233
    [Google Scholar]
  73. Roberts DE. 2003. Child welfare and civil rights. Univ. Ill. Law Rev. 2003:171–82
    [Google Scholar]
  74. Rose H. 2019. How the Trump administration's plan to limit disparate impact liability would undermine the Fair Housing Act's goal of promoting residential integration Work. Pap Law School, Loyola Univ. Chicago, IL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3464555
  75. Sacarny A, Barnett ML, Le J, Tetkoski F, Yokum D, Agrawal S 2018. Effect of peer comparison letters for high-volume primary care prescribers of quetiapine in older and disabled adults: a randomized clinical trial. JAMA Psychiatry 75:1003–11
    [Google Scholar]
  76. Sacarny A, Yokum D, Finkelstein A, Agrawal S 2016. Medicare letters to curb overprescribing of controlled substances had no detectable effect on providers. Health Aff 35:471–79
    [Google Scholar]
  77. Salganik M. 2019. Bit by Bit: Social Research in the Digital Age Princeton, NJ: Princeton Univ. Press
  78. Sandefur RL. 2008. Access to civil justice and race, class, and gender inequality. Annu. Rev. Sociol. 34:339–58
    [Google Scholar]
  79. Sandefur RL. 2014. Accessing justice in the contemporary USA: findings from the community needs and services study Doc., Am. Bar Found. Chicago: http://www.americanbarfoundation.org/uploads/cms/documents/sandefur_accessing_justice_in_the_contemporary_usa._aug._2014.pdf
  80. Sandefur RL. 2015. What we know and need to know about the legal needs of the public. S. C. Law Rev. 67:443–60
    [Google Scholar]
  81. Selbst AD. 2017. Disparate impact in big data policing. Ga. Law Rev. 52:109–95
    [Google Scholar]
  82. Self-Represent. Litig. Netw 2019. SRLN brief: How many SRLs? https://www.srln.org/node/548/srln-brief-how-many-srls-srln-2015
  83. Vagle JL. 2016. Tightening the OODA loop: police militarization, race, and algorithmic surveillance. Mich. J. Race Law 22:101–37
    [Google Scholar]
  84. Vaithianathan R, Kulick E, Putnam-Hornstein E, Benavides Prado D 2019. Allegheny Family Screening Tool: Methodology, Version 2 Pittsburgh, PA: Allegheny County
  85. Van Zee A. 2009. The promotion and marketing of OxyContin: commercial triumph, public health tragedy. Am. J. Public Health 99:221–27
    [Google Scholar]
  86. Verhulst S, Sangokoya D. 2015. Data collaboratives: exchanging data to improve people's lives. Medium April 22. https://medium.com/@sverhulst/data-collaboratives-exchanging-data-to-improve-people-s-lives-d0fcfc1bdd9a
  87. Whittaker M, Crawford K, Dobbe R, Fried G, Kaziunas E et al. 2018. AI Now Report 2018 Rep., AI Now Inst., N.Y. Univ. New York:
  88. Ye T, Johnson R, Fu S, Copeny J, Donnelly B et al. 2019. Using machine learning to help vulnerable tenants in New York City. Proceedings of the Conference on Computing & Sustainable Societies248–58 New York: Assoc. Comput. Mach.
    [Google Scholar]
  89. Zalnieriute M, Burton L, Boughey J, Bennett Moses L, Logan S 2020. From rule of law to statute drafting: legal issues for algorithms in government decision-making. Cambridge Handbook of the Law of Algorithms W Barfield 19–30 Cambridge, UK: Cambridge Univ. Press In press
    [Google Scholar]
/content/journals/10.1146/annurev-lawsocsci-061020-050543
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error