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

Algorithmic audits (or 'AI audits') are an increasingly popular mechanism for algorithmic
accountability; however, they remain poorly defined. Without a clear understanding of audit …

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 …

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

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 …

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 this paper, we present the Algorithmic Audit (AA) of REM! X, a personalized well-being
recommendation app developed by Telefónica Innovación Alpha. The main goal of the AA …

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 …

External audits of AI systems are increasingly recognized as a key mechanism for AI
governance. The effectiveness of an audit, however, depends on the degree of access …

This paper introduces reviewability as a framework for improving the accountability of
automated and algorithmic decisionmaking (ADM) involving machine learning. We draw on …