Algorithmic decision-making increasingly shapes people's daily lives. Given that such
autonomous systems can cause severe harm to individuals and social groups, fairness …

This article reviews the recent literature on algorithmic fairness, with a particular emphasis
on credit scoring. We discuss human versus machine bias, bias measurement, group versus …

Research on the ethics of algorithms has grown substantially over the past decade.
Alongside the exponential development and application of machine learning algorithms …

The increasing implementation of and reliance on machine-learning (ML) algorithms to
perform tasks, deliver services and make decisions in health and healthcare have made the …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

“AI as a Service”(AIaaS) is a rapidly growing market, offering various plug-and-play AI
services and tools. AIaaS enables its customers (users)—who may lack the expertise, data …

There is growing concern that decision-making informed by machine learning (ML)
algorithms may unfairly discriminate based on personal demographic attributes, such as …

Among the myriad of technical approaches and abstract guidelines proposed to the topic of
AI bias, there has been an urgent call to translate the principle of fairness into the …

Despite being the fastest-growing field because of its ability to enhance competitive
advantage, there are concerns about the inherent fairness in Artificial Intelligence (AI) …

In this paper, we compare two different approaches to estimate the credit risk for small-and
mid-sized businesses (SMBs), namely a classic parametric approach, by fitting an ordered …