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 …

Audits are critical mechanisms for identifying the risks and limitations of deployed artificial
intelligence (AI) systems. However, the effective execution of AI audits remains incredibly …

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

Artificial intelligence (AI) technologies have become the key driver of innovation in society.
However, numerous vulnerabilities of AI systems can lead to negative consequences for …

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

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 …

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 …

Motivated by the extensive documented disparate harms of artificial intelligence (AI), many
recent practitioner-facing reflective tools have been created to promote responsible AI …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

A series of recent developments points towards auditing as a promising mechanism to
bridge the gap between principles and practice in AI ethics. Building on ongoing discussions …