HOW DOES CRYSTAL KNOW? FOLK THEORIES AND TRUST IN PREDICTIVE ALGORITHMS THAT ASSESS INDIVIDUAL PERSONALITY AND COMMUNICATION PREFERENCES

  • Tony Liao University of Cincinnati
Keywords: Hyper-Personal Algorithms, Perception, Folk Theories, CrystalKnows

Abstract

In recent years, there has been a rise in predictive algorithms that focus on individual preferences and psychometric assessments. The idea is that an individual social media presence may give off unconscious cues or indicators of a person's personality. While there has been a growing body of research into people's reactions, perceptions, and folk theories of how algorithms work, there has been a growing need for research into these hyper-personal algorithms and profiles. This study focuses on a company called CrystalKnows, which purports to have the largest database of personality profiles in the world, many of which are generated without an individual's explicit consent. Through qualitative interviews (n=31) with people after being presented with their own profile, this study explores how people perceive the profiles, where they believe the information is coming from, and what contexts they would be comfortable with their profile being used. Crystal profiles also contain predictions about how people will communicate and potentially work together in teams with people of other personality dispositions, which also raises concerns about inaccurate assessments or discrimination based on these profiles. The findings from this study and how people rationalize these algorithms not only builds on our understanding of algorithmic perception and folk theories, but also has important practical implications for the trust in these systems and the continued deployment of hyper-personal predictive algorithms.

Published
2019-10-31
How to Cite
Liao, T. (2019). HOW DOES CRYSTAL KNOW? FOLK THEORIES AND TRUST IN PREDICTIVE ALGORITHMS THAT ASSESS INDIVIDUAL PERSONALITY AND COMMUNICATION PREFERENCES. AoIR Selected Papers of Internet Research, 2019. https://doi.org/10.5210/spir.v2019i0.11001
Section
Papers L