… , the development and deployment of insufficiently fair systems of artificial intelligence (AI) …
fairness in machine learning through the lens of healthcare, and discuss how algorithmic …

… Artificial intelligence has exposed pernicious bias within health data that constitutes … We
highlight challenges to the ethical and empirical efficacy of solutions of algorithmic fairness that …

… algorithmic fairness in the context of computational medicine, which aims at improving medicine
… the development and deployment of machine learning algorithms in healthcare. These …

… algorithmic fairness at the population level while making clear why our considerations matter
for medical … The right to refuse diagnostics and treatment planning by artificial intelligence. …

… deployment of many artificial intelligence (AI) systems in health- care, algorithm fairness is
a … is also a longstanding discussion in many other domains, with algorithmic fairness and AI …

… Next, we suggest strategies to reduce bias such as using diverse data, validating algorithms,
and educating clinicians and patients regarding AI. We then discuss ethical and legal …

… Artificial intelligence has a huge array of current and potential applications in healthcare
and medicine. Ethical issues arising due to algorithmic biases are one of the greatest …

… First, lack of a clear definitions and standard of “fairness”, second, insufficient contextual …
define algorithmic bias in health systems, a risk inherent in AI, explore its implications for health …

… algorithmic bias. Even though such issues might appear as being merely practical or technical
ones, their closer examination uncovers questions of value, fairness … , fairness and health …

… the context of global health, we describe three basic criteria (Appropriateness, Fairness, and
Bias… Fairness criteria can be applied to both types of algorithms; however, for the purpose of …