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 …

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 …

Forming a reliable judgement of a machine learning (ML) model's appropriateness for an
application ecosystem is critical for its responsible use, and requires considering a broad …

Algorithms are becoming more widely used in business, and businesses are becoming
increasingly concerned that their algorithms will cause significant reputational or financial …

An increasing number of regulations propose 'AI audits' as a mechanism for achieving
transparency and accountability for artificial intelligence (AI) systems. Despite some …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

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 …

Large language models (LLMs) are increasingly becoming all-powerful and pervasive via
deployment in sociotechnical systems. Yet these language models, be it for classification or …

Oyeniran, Adewusi, Adeleke, Akwawa, & Azubuko, P. 115-126 Page 116 implementing
robust auditing processes. We also review existing ethical guidelines and frameworks, such …

In recent years there has been much talk about the advancements in artificial intelligence
(AI). Large strides have been made particularly in the area of machines learning algorithms …