Big Data and Trust in Public Policy Automation

Waggoner Philip D. (), Kennedy Ryan, Le Hayden and Shiran Myriam
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Waggoner Philip D.: University of Chicago, Chicago, IL, USA
Kennedy Ryan: University of Houston, Houston, TX, USA
Le Hayden: University of Michigan, Ann Arbor, MI, USA
Shiran Myriam: University of Houston, Houston, TX, USA

Statistics, Politics and Policy, 2019, vol. 10, issue 2, 115-136

Abstract: Big data is everywhere, both in and out of public policy. Though a rich data source, what is the impact of big data beyond the research community? We suggest such that invoking big data-related terms acts as a heuristic for assumed algorithmic quality. Such an assumption leads to greater trust in automation in public policy decision-making. We test this “big-data-effect” expectation using four tests including a conjoint experiment embedded in a recently fielded survey experiment. We find strong evidence that indeed, big data-related terms act as powerful signals of assumed quality where respondents consistently prefer algorithms with bigger data behind them, absent any mention of predictive accuracy or definitions of key terms (e.g. “training features”). As we expect this big-data-effect is likely not beholden to public policy, we encourage more research in this vein to deepen an understanding of the influence of big data on modern society.

Date: 2019
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DOI: 10.1515/spp-2019-0005

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