Labor & Employment

The description of a combination of technologies as ‘artificial intelligence’ (AI) is misleading. To ascribe intelligence to a statistical model without human attribution points towards an attempt at shifting legal, social, and ethical responsibilities to machines. This paper exposes the...

Predictive analytics and artificial intelligence are applied widely across law enforcement agencies and the criminal justice system. Despite criticism that such tools reinforce inequality and structural discrimination, proponents insist that they will nonetheless improve the equality and fairness of outcomes...

Abstract Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to...

Undergraduate Admissions Graduate & Professional Admissions Financial Aid

Abstract The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand,...

By: Alyssa Shaw, Volume 109 Staff Member If the progress towards closing the gender wage gap continues on the trends of the last few years, women will not be compensated equally to men until at least 2067—over a century after...

This paper divides the application of AI in education into three categories, namely, students-oriented AI, teachers-oriented AI and school mangers -oriented AI, which focuses on the individualized self-adaptive learning of students, the assisted teaching of teachers and the service management...

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, concerns about algorithmic bias and fairness remain central to ethical AI deployment. This paper explores...

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 sets the foundation for how the law will address this issue. I critically analyse elements...

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 gender) and the correction of data bias. In terms of determining sensitive attributes, existing studies...

The rapid integration of artificial intelligence (AI) into critical decision-making domains—such as healthcare, finance, law enforcement, and hiring—has raised significant ethical concerns regarding bias and fairness. Algorithmic decision-making systems, if not carefully designed and monitored, risk perpetuating and amplifying societal...

PurposeThis study examines the impact of data bias and algorithmic discrimination on individual credit accessibility in China’s financial system. It aims to align financial inclusion and equity goals with statistical fairness conditions by constructing fairness metrics from multiple dimensions. The...