Virtual Room 1: Data Access

Date: 

Wednesday, July 15, 2020, 12:00pm to 1:30pm

Statistically Valid Inferences from Privacy Protected Data

Georgina Evans, Gary King, Margaret Schwenzfeier and Abhradeep Thakurta

Hidden in Plain Sight? Detecting Electoral Irregularities Using Statutory Results

Zach Warner, J. Andrew Harris, Michelle Brown and Christian Arnold

 

Chair: Suzanna Linn (Penn State University)

 

Co-Host: Anwar Mohammed (McMaster University)

Statistically Valid Inferences from Privacy Protected Data

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Author(s): Georgina Evans, Gary King, Margaret Schwenzfeier and Abhradeep Thakurta

Discussant: James Honaker (Harvard University)

 

Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data and statistical validity guarantees for researchers seeking population-level insights from it. We build on the standard of "differential privacy" but, unlike most such approaches, we also correct for the serious statistical biases induced by privacy-preserving procedures, provide a proper accounting for statistical uncertainty, and impose minimal constraints on the choice of data analytic methods and types of quantities estimated. Our algorithm is easy to implement, simple to use, and computationally efficient; we also offer open source software to illustrate all our methods.

Hidden in Plain Sight? Detecting Electoral Irregularities Using Statutory Results

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Author(s): Zach Warner, J. Andrew Harris, Michelle Brown and Christian Arnold

Discussant: Walter Mebane (University of Michigan)

 

Confidence in election results is a central pillar of democracy, but in many developing countries, elections are plagued by a number of irregularities. Such problems can include incredible vote margins, sky-high turnout, and statutory forms that indicate manual edits. Recent scholarship has sought to identify these problems and use them to quantify the magnitude of election fraud in countries around the world. In this paper, we argue that this literature suffers from its reliance on an ideal election as a baseline case, and delineate an alternative data-generating process for the irregularities often seen in developing democracies: benign human error. Using computer vision and deep learning tools, we identify statutory irregularities for each of 30,000 polling stations in Kenya’s 2013 presidential election. We show that these irregularities are uncorrelated with election outcomes and do not reflect systematic fraud. Our findings suggest that scholars of electoral integrity should take care to ensure that their methods are sensitive to context and account for the substantial challenges of administering elections in developing democracies.


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