Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors

TL;DR

This paper highlights the importance of everyday users in detecting harmful algorithmic behaviors through their routine interactions, emphasizing the need to integrate bottom-up auditing with formal methods for more effective oversight.

Contribution

It introduces the concept of everyday algorithm auditing, analyzing real-world cases and proposing design considerations to enhance user-driven detection of problematic behaviors.

Findings

Users can detect issues that formal audits may miss

Everyday interactions reveal diverse harmful behaviors

Designing platforms for user auditing can improve system oversight

Abstract

A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.