The use of machine learning systems for decision-support in healthcare may exacerbate
health inequalities. However, recent work suggests that algorithms trained on sufficiently …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

The machine learning community has become alert to the ways that predictive algorithms
can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts …

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring,
and criminal justice. In response, computer scientists have developed myriad definitions of …

From machine learning (ML) and computer vision to robotics and natural language
processing, the application of data science and artificial intelligence (AI) is expected to …

Machine learning in medicine: Addressing ethical challenges | PLOS Medicine Skip to main
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Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are
providing new opportunities to operationalize previously untapped and rapidly growing …

The rapid advancement and deployment of deep learning-enabled medical systems have
necessitated the development of robust ethical frameworks to address potential challenges …

Machine learning (ML) systems play an increasingly relevant role in medicine and
healthcare. As their applications move ever closer to patient care and cure in clinical …

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …