The Performativity of AI-powered Protest Detection: How AI Creates a Racialized Protest and Why Looking for ‘Bias’ Is Not a Solution

TY - JOUR

T1 - The Performativity of AI-powered Protest Detection

T2 - How AI Creates a Racialized Protest and Why Looking for ‘Bias’ Is Not a Solution

AU - Frabetti, Federica

AU - Drage, Eleanor

PY - 2023/3/27

Y1 - 2023/3/27

N2 - This article builds a theoretical framework with which to confront the racializing capabilities of artificial intelligence (AI)-powered real-time Event Detection and Alert Creation (EDAC) software when used for protest detection. It is well-known that many AI-powered systems exacerbate social inequalities by racializing certain groups and individuals. We propose the feminist concept of performativity, as defined by Judith Butler and Karen Barad, as a more comprehensive way to expose and contest the harms wrought by EDAC than that of other “de-biasing” mechanisms. Our use of performativity differs from and complements other Social Studies of Science and Technology (STS) work because of its rigorous approach to how iterative, citational, and material practices produce the effect of race. We focus on Geofeedia and Dataminr, two EDAC companies that claim to be able to “predict” and “recognize” the emergence of dangerous protests, and show how their EDAC tools performatively produce the phenomena which they are supposed to observe. Specifically, we argue that this occurs because these companies and their stakeholders dictate the thresholds of (un)intelligibility, (ab)normality, and (un)certainty by which these tools operate and that this process is oriented toward the production of commercially actionable information.

AB - This article builds a theoretical framework with which to confront the racializing capabilities of artificial intelligence (AI)-powered real-time Event Detection and Alert Creation (EDAC) software when used for protest detection. It is well-known that many AI-powered systems exacerbate social inequalities by racializing certain groups and individuals. We propose the feminist concept of performativity, as defined by Judith Butler and Karen Barad, as a more comprehensive way to expose and contest the harms wrought by EDAC than that of other “de-biasing” mechanisms. Our use of performativity differs from and complements other Social Studies of Science and Technology (STS) work because of its rigorous approach to how iterative, citational, and material practices produce the effect of race. We focus on Geofeedia and Dataminr, two EDAC companies that claim to be able to “predict” and “recognize” the emergence of dangerous protests, and show how their EDAC tools performatively produce the phenomena which they are supposed to observe. Specifically, we argue that this occurs because these companies and their stakeholders dictate the thresholds of (un)intelligibility, (ab)normality, and (un)certainty by which these tools operate and that this process is oriented toward the production of commercially actionable information.

KW - AI Bias

KW - Racist AI

KW - performativity

KW - Predictive policing

KW - protest detection

U2 - 10.1177/01622439231164660

DO - 10.1177/01622439231164660

M3 - Article

SN - 0162-2439

VL - 49

SP - 1045

EP - 1072

JO - Science, Technology, & Human Values (ST&HV) : journal of the Society for Social Studies of Science

JF - Science, Technology, & Human Values (ST&HV) : journal of the Society for Social Studies of Science

IS - 5

ER -