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ABSTRACT. I draw on a substantial body of theoretical and empirical research on the inherent regulatory capacity of data-driven automated decision-making, and to explore this, I inspected, used, and replicated survey data from Pew Research Center, performing analyses and making estimates regarding % of Facebook users who say they understand not at all/not very/somewhat/very well why certain posts are included in their news feed and others are not, % of U.S. adults who say that it is possible for computer programs to make decisions without human bias/computer programs will always reflect bias of designers (by age group), and % of Facebook users with no assigned category/fewer than 10 categories/10–20 categories/21+ categories listed on their “ad preferences” page. Structural equation modeling was used to analyze the data and test the proposed conceptual model.

Keywords: governance; analytical algorithm; data-driven automated decision-making

How to cite: Tooby, Chelsea (2019). “Governance Mechanisms of Analytical Algorithms: The Inherent Regulatory Capacity of Data-driven Automated Decision-Making,” Contemporary Readings in Law and Social Justice 11(1): 39–44. doi:10.22381/CRLSJ11120196

Received 8 March 2019 • Received in revised form 4 July 2019
Accepted 7 July 2019 • Available online 15 July 2019

Chelsea Tooby
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The Cognitive Labor Institute,
New York City, NY, USA

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