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The Courts as Gatekeeper of Big Data Evidence

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Big Data Analytics in U.S. Courts

Abstract

From 1923 until 1993, the admissibility of scientific evidence in the federal court system was governed by the standard set forth in Frye v. United States. In applying this standard, courts examined whether the proffered evidence had “gained general acceptance” in the particular field. In 1993, the Supreme Court decided Daubert v. Merrell Dow Pharmaceuticals, Inc., and the court announced a new standard of admissibility for scientific evidence. This chapter illustrates how the Daubert standard has been applied to data analyses in US courts and its implication for Big Data as related cases become more frequent in the courts. Significantly, technological changes that make Big Data possible threaten to change the basis of current statistical reasoning and the basis upon which courts make decisions.

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Notes

  1. 1.

    , Inc., 509 U.S. 579 (1993).

  2. 2.

    Rob Kitchen, Big Data, new epistemologies and paradigm shifts, Big Data and Society, April 14, 2014.

  3. 3.

    Tyson Foods, Inc. v. Bouaphakeo, 577 U.S. 136 S. Ct. 1036 (2016).

  4. 4.

    Chris Anderson, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, Wired, June 23, 2008.

  5. 5.

    Norman Fenton, Martin Neil, and Daniel Berger, Bayes and the Law, Annual Review of Statistics and Its Application 2016 3:1, 51–77.

  6. 6.

    Mcgrayne, Sharon Bertsch, The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. 2011, Yale University Press.

  7. 7.

    See Consumer Financial Protection Bureau, Using publicly available information to proxy for unidentified race and ethnicity. September 2014.

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Correspondence to Dwight Steward .

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Steward, D., Cavazos, R. (2019). The Courts as Gatekeeper of Big Data Evidence. In: Big Data Analytics in U.S. Courts. Palgrave Advances in the Economics of Innovation and Technology. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-31780-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-31780-5_4

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  • Publisher Name: Palgrave Pivot, Cham

  • Print ISBN: 978-3-030-31779-9

  • Online ISBN: 978-3-030-31780-5

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