Quality follows upgrading
Academic
Abstract In the debate on copyright exceptions permitting text and data mining (“TDM”) for the development of generative AI systems, the so-called “three-step test” has …
Abstract The advancement of machine learning and artificial intelligence (AI) technology has fundamentally altered the production and ownership of works, including video games. That is …
Algorithmic decision-making systems have become pervasive across critical domains including employment, housing, healthcare, and criminal justice. While these systems promise enhanced efficiency and objectivity, they …
The integration of Artificial Intelligence (AI) in recruitment processes has revolutionized hiring by increasing efficiency, reducing time-to-hire, and enabling data-driven decision-making. However, despite these advancements, …
Cloud-based machine learning systems are increasingly used in sectors such as healthcare, finance, and public services, where they influence decisions with significant social consequences. While …
Artificial intelligence systems deployed through cloud infrastructure have transformed numerous sectors while simultaneously raising critical ethical concerns regarding bias and fairness. This article examines the …
This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which …
Machine learning fairness enhancement methods based on data bias correction are usually divided into two processes: The determination of sensitive attributes (such as race and …
AI-driven healthcare systems perpetuate gendered and racialised health inequalities, misdiagnosing marginalised populations due to historical exclusions in medical research and dataset construction. These disparities are …