Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

TL;DR

This systematic review synthesizes over 60 studies on algorithm audits, highlighting problematic behaviors like discrimination and distortion, and offers insights for future research and best practices in auditing for algorithmic justice.

Contribution

The paper provides a comprehensive synthesis of algorithm audit studies, categorizing behaviors, domains, methods, and organizations, and offers guidelines for effective future audits.

Findings

Algorithms exhibit problematic behaviors such as discrimination and distortion.

Certain domains like advertising and organizations like TikTok need more audit attention.

Successful audits share common ingredients and contribute to algorithmic justice.

Abstract

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.