ABSTRACT. We inspect the relevant literature on the decision-making logic of big data algorithmic analytics, providing both quantitative evidence on trends and numerous in-depth empirical examples. Building our argument by drawing on data collected from Pew Research Center, we performed analyses and made estimates regarding % of users who say they frequently/sometimes see on social media posts that are overly dramatic or exaggerated/people making accusations or starting arguments without having all the facts/posts that teach you something useful you had not known before/posts that appear to be about one thing but turn out to be about something else and % of users who say, after being directed to view their Facebook “ad preferences” page, that they did not know Facebook maintained this list of their interests and traits/they are not comfortable with Facebook compiling this information/the listings do not very or at all accurately represent them. Data collected from 4,200 respondents are tested against the research model by using structural equation modeling.

Keywords: decision-making logic; big data algorithmic analytics; machine learning

How to cite: Ashander, Laura, Jana Kliestikova, Pavol Durana, and Jaromir Vrbska (2019). “The Decision-Making Logic of Big Data Algorithmic Analytics,” Contemporary Readings in Law and Social Justice 11(1): 57–62. doi:10.22381/CRLSJ11120199

Received 6 March 2019 • Received in revised form 5 July 2019
Accepted 12 July 2019 • Available online 15 July 2019