Public Understanding of Algorithms and Trust in Platforms

24 Pages Posted: 16 Feb 2018 Last revised: 8 Aug 2018

See all articles by Grant Blank

Grant Blank

University of Oxford - Oxford Internet Institute

Bianca Reisdorf

University of North Carolina (UNC) at Charlotte - College of Arts & Sciences; Michigan State University - Quello Center

Date Written: February 12, 2018

Abstract

Objectives of the Paper: Algorithms affect large parts of people’s lives (Diakopoulos, 2014; Gillespie, 2012; Just & Latzer, 2016). Prominent examples are the algorithms that guide search engine results and what social media users see in their newsfeed on platforms such as Facebook. After the Brexit vote in Britain and the election of Donald Trump in the US in 2016, much attention was drawn to the potential influence of algorithms that may expose internet users to selective and limited content rather than the diverse information that is generally available online. Although the exact details of how corporate algorithms work are proprietary and therefore somewhat of a “black box”, some of the core factors that influence search results are location, previous search history, and the popularity and user experience (Google, 2017; Kliman-Silver et al., 2015). There is little research about how well Internet users understand algorithms and how this (lack of) knowledge affects their utilization and trust of platforms, such as search engines or social media. This will be the first quantitative study of algorithmic literacy. The paper examines these research questions: What is the effect of algorithmic literacy on Internet use and skills? (How) Does algorithmic literacy affect trust in algorithm-based platforms? (How) Does algorithmic literacy affect amount of use of algorithm-based platforms? Methods and data: We employ multivariate regressions using data from the Quello Search Project, a study of media use and politics collected in January 2017 in the United States. The 2,018 cases are a random sample of the online population. Relevance: Algorithms play an increasingly central role in our lives yet we know little about how much average Internet user understands about algorithms and how they work. The results will further our understanding of algorithmic literacy among Internet users and its consequences for trust and use practices. This has implications for policies regarding platforms that are based on algorithms. Preliminary Results:Preliminary analyses show that algorithmic literacy has a significant positive impact on platform trust for search engines; however the amount of internet use and skill using a search engine have a stronger effect. Socio-demographic factors, such as age and lifestage, have relatively little impact. Further analyses will refine these results. References: Diakopoulos, N. (2014). Algorithmic Accountability Reporting: On the Investigation of Black Boxes. New York, NY: Columbia University Academic Commons. --Gillespie, T. (2012). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media Technologies: Essays on Communication, Materiality, and Society (pp. 167–194). London, England: The MIT Press. --Google (2017). How Search Works: How Search algorithms work. Available online: https://www.google.com/search/howsearchworks/algorithms/ --Just, N., & Latzer, M. (2017). Governance by algorithms: reality construction by algorithmic selection on the Internet. Media, Culture & Society, 39(2), 238-258. --Kliman-Silver, C., Hannak, A., Lazer, D., Wilson, C., & Mislove, A. (2015, October). Location, location, location: The impact of geolocation on web search personalization. In Proceedings of the 2015 ACM Conference on Internet Measurement Conference (pp. 121-127). ACM.

Keywords: algorithm, algorithmic literacy, media literacy, social media, search engine, internet

JEL Classification: L86

Suggested Citation

Blank, Grant and Reisdorf, Bianca, Public Understanding of Algorithms and Trust in Platforms (February 12, 2018). TPRC 46: The 46th Research Conference on Communication, Information and Internet Policy 2018. Available at SSRN: https://ssrn.com/abstract=3122665

Grant Blank (Contact Author)

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

Bianca Reisdorf

University of North Carolina (UNC) at Charlotte - College of Arts & Sciences ( email )

Charlotte, NC
United States

Michigan State University - Quello Center ( email )

406 Communication Arts Building
404 Wilson Road
East Lansing, MI 48824-1212
United States

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