“Crystal Is Creepy, but Cool”: Mapping Folk Theories and Responses to Automated Personality Recognition Algorithms

摘要

This article examines Crystal Knows, a company that generates automated personality profiles through an algorithm and sells access to their database. These algorithms are the result of a long line of research into computational and predictive algorithms that track social media practices and uses them to infer individual characteristics and make psychometric assessments. Although it is now computationally possible, these algorithms are not widely known or understood by the general public. Little is known about how people would respond to them, particularly when they do not even know their online activities are being assessed by the algorithm. This study examines how people construct “snap” folk theories about the ways personality algorithms operate as well as how they react when shown their outputs. Through qualitative interviews ( n ?=?37) with people after being presented with their own profile, this study identifies a series of folk theories that people came up with to explain the personality algorithm across four dimensions (data source, scope, collection process, and outputs). In addition, this study examined how those folk theories contributed to certain reactions, fears, and justifications people had about the algorithm. This study builds on our theoretical understanding of folk theory literature as well as certain limitations of algorithmic transparency/sovereignty when these types of inferential and predictive algorithms get coupled with people’s hopes and fears about employment, hiring, and promotion.