This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

The study comprehensively reviews artificial intelligence (AI) techniques for addressing
algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) …

Despite the rapid adoption of technology in human resource departments, there is little
empirical work that examines the potential challenges of algorithmic decision-making in the …

The ever-increasing adoption of AI technologies in the hiring landscape to enhance human
resources efficiency raises questions about algorithmic decision-making's implications in …

More organizations use AI in the hiring process than ever before, yet the perceived ethicality
of such processes seems to be mixed. With such variation in our views of AI in hiring, we …

Affective computing has an unprecedented potential to change the way humans interact with
technology. While the last decades have witnessed vast progress in the field, multimodal …

In the future, large language models (LLMs) may enhance the delivery of healthcare, but
there are risks of misuse. These methods may be trained to allocate resources via unjust …

Large language models (LLMs) have emerged as an integral part of modern societies,
powering user-facing applications such as personal assistants and enterprise applications …

With the ever-growing reliance on Artificial Intelligence (AI) across diverse domains, there is
an increasing concern surrounding the possibility of biases and unfairness inherent in AI …

Introduction Gender biases in hiring decisions remain an issue in the workplace. Also,
current gender balancing techniques are scientifically poorly supported and lead to …