Folk theories of algorithms: Understanding digital irritation

Brita Ytre-Arne,Hallvard Moe

doi:10.1177/0163443720972314

Abstract

This article draws on the framework of “folk theories” to analyze how people perceive algorithms in the media. Taking algorithms as a prime case to investigate how people respond to datafication in everyday media use, we ask how people perceive positive and negative consequences of algorithms. To answer our question, we conduct qualitative thematic analysis of open-ended answers from a 2019 representative survey among Norwegians, identifying five folk theories: algorithms are confining, practical, reductive, intangible, and exploitative. We situate our analysis in relation to different application of folk theory approaches, and discuss our findings in light of emerging work on perceptions of algorithms and critiques of datafication, including the concept digital resignation. We conclude that rather than resignation, digital irritation emerges as a central emotional response, with a small but significant potential to inspire future political action against datafication.

Highlights

This article draws on the framework of “folk theories” to analyze how people perceive algorithms in the media
Algorithms are integral to media experiences, routinely encountered when navigating social media newsfeeds, targeted advertising, streaming services or personalized media
Algorithmic media draw on the systematic exploitation of user data often referred to as datafication, that is “the requirement, not just the possibility, that every variation in the texture of human experience be translated into data for counting and processing”

Summary

Introduction

This article draws on the framework of “folk theories” to analyze how people perceive algorithms in the media. The potential consequences for media’s role in democracy, and for democratic society in general, are grave To better understand such consequences, we need insights into how users interact with structures of datafication (Vaughan-Williams and Stevens, 2016), to study everyday experiences with data (Kennedy, 2018; Livingstone, 2018) and advance a social critique of datafication (Couldry, 2014). Surveillance realism is defined as a “lack of transparency and knowledge in conjunction with the active normalization of surveillance through discursive practices and institutional sanctions” (Dencik and Cable, 2017: 777), while digital resignation emphasizes that “feelings of resignation are a rational emotional response in the face of undesirable situations that individuals believe they cannot combat” (Draper and Turow, 2019: 5) Do these concepts grasp user reactions to datafication, and if so, are feelings of resignation more generalized than specific privacy issues? Are we right to assume that people feel algorithms to be opaque and unavoidable? Are algorithms considered mysterious, predictable – or useful? How do people perceive positive and negative aspects of algorithms?

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Conclusion