ABSTRACT. This article reviews and advances existing literature concerning big data-driven algorithmic decision-making. Using and replicating data from Deloitte and Pew Research Center, I performed analyses and made estimates regarding % of Facebook users who have not/have intentionally tried to influence the content that appears on their news feed (by age group), % of U.S. adults who say the content on social media does/does not provide an accurate picture of how society feels about important issues, % of social media users who say it is not at all acceptable/not very acceptable/somewhat acceptable/very acceptable for social media sites to use data about them and their online activities to recommend events in their area/recommend someone they might want to know/show them ads for products and services/show them messages from political campaigns, and among U.S. social media users, the % of who say it would be hard to give up/not hard to give up social media (by age group). Data were analyzed using structural equation modeling.

Keywords: automation; autonomy; big data; algorithmic decision-making; human behavior

How to cite: Furnham, Philipp (2019). “Automation and Autonomy of Big Data-driven Algorithmic Decision-Making,” Contemporary Readings in Law and Social Justice 11(1): 51–56. doi:10.22381/CRLSJ11120198

Received 4 March 2019 • Received in revised form 7 July 2019
Accepted 9 July 2019 • Available online 15 July 2019

Philipp Furnham
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Social Analytics Laboratory
at CLI, Gothenburg, Sweden

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