Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI
applications hold promise as tools to predict surgical outcomes, assess technical skills, or …

Turning principles into practice is one of the most pressing challenges of artificial
intelligence (AI) governance. In this Perspective, we reflect on a governance initiative by one …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Machine learning models risk encoding unfairness on the part of their developers or data
sources. However, assessing fairness is challenging as analysts might misidentify sources …

A growing number of studies on fairness in artificial intelligence (AI) use the notion of
intersectionality to measure AI fairness. Most of these studies take intersectional fairness to …

A key insight from research on organizational justice is that fairness is in the eye of the
beholder. With increasing discussions–especially among computer scientists and …

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …

Bias is a key issue in expert and public discussions about Artificial Intelligence (AI). While
some hope that AI will help to eliminate human bias, others are concerned that AI will …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

Ensuring fairness in artificial intelligence (AI) is important to counteract bias and
discrimination in far-reaching applications. Recent work has started to investigate how …