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 …

Recent years have seen growing interest among both researchers and practitioners in user-
engaged approaches to algorithm auditing, which directly engage users in detecting …

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 …

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 …

Algorithm audits are powerful tools for studying black-box systems without direct knowledge
of their inner workings. While very effective in examining technical components, the method …

Algorithms are ubiquitous and critical sources of information online, increasingly acting as
gatekeepers for users accessing or sharing information about virtually any topic, including …

Rising concern for the societal implications of artificial intelligence systems has inspired a
wave of academic and journalistic literature in which deployed systems are audited for harm …

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes
mostly focus on detecting harmful algorithmic biases. While these strategies have proven to …

Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

In recent years, the ethical impact of AI has been increasingly scrutinized, with public
scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This …