Toward User-Driven Algorithm Auditing: Investigating users' strategies for uncovering harmful algorithmic behavior

Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic behaviors that formal auditing approaches fail to detect. However, it is not well understood how users are often able to be so effective, nor how we might support more effective user-driven auditing. To investigate, we conducted a series of think-aloud interviews, diary studies, and workshops, exploring how users find and make sense of harmful behaviors in algorithmic systems, both individually and collectively. Based on our findings, we present a process model capturing the dynamics of and influences on users’ search and sensemaking behaviors. We find that 1) users’ search strategies and interpretations are heavily guided by their personal experiences with and exposures to societal bias; and 2) collective sensemaking amongst multiple users is invaluable in user-driven algorithm audits. We offer directions for the design of future methods and tools that can better support user-driven auditing.