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ABSTRACT. I inspect the relevant literature on data-driven algorithmic decision-making, providing both quantitative evidence on trends and numerous in-depth empirical examples. Building my argument by drawing on data collected from HubSpot, Pew Research Center, and Statista, I performed analyses and made estimates regarding % of users who say they frequently/sometimes see content on social media that makes them feel amused/angry/connected/inspired/depressed/lonely, % of users who say they more often see people being mean or bullying/kind or supportive/trying to be deceptive/trying to point out inaccurate info when using social media sites, and % of users who say it would be very/somewhat difficult/easy for social media sites to figure out their race or ethnicity/hobbies and interests/political affiliation/religious beliefs. Data collected from 4,800 respondents are tested against the research model by using structural equation modeling.

Keywords: automated digital system; data-driven algorithmic decision-making; society

How to cite: Androniceanu, Armenia (2019). “Using Automated Digital Systems to Thoroughly Regulate Social Governance: Monitoring and Behavior Modification through Data-driven Algorithmic Decision-Making,” Contemporary Readings in Law and Social Justice 11(1): 63–68. doi:10.22381/CRLSJ111201910

Received 8 March 2019 • Received in revised form 7 July 2019
Accepted 9 July 2019 • Available online 15 July 2019

Armenia Androniceanu
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University of Social Sciences, Lodz, Poland;
The Bucharest University of Economic Studies, Romania

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