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Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors
It is argued 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.
Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior
It is found 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.