Background There is a growing concern about artificial intelligence (AI) applications in
healthcare that can disadvantage already under-represented and marginalised groups (eg …

The field of automated machine learning (AutoML) introduces techniques that automate
parts of the development of machine learning (ML) systems, accelerating the process and …

Applied machine learning (ML) has not yet coalesced on standard practices for research
ethics. For ML that predicts mental illness using social media data, ambiguous ethical …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

A central goal of algorithmic fairness is to reduce bias in automated decision making. An
unavoidable tension exists between accuracy gains obtained by using sensitive information …

The issue of bias and fairness in healthcare has been around for centuries. With the
integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased …

The increasing application of artificial intelligence (AI) to healthcare raises both hope and
ethical concerns. Some advanced machine learning methods provide accurate clinical …

Machine learning systems have become integrated into some of the most vital decision-
making aspects of humanity, including hiring decisions, loan applications, and automobile …

There are many competing definitions of what statistical properties make a machine learning
model fair. Unfortunately, research has shown that some key properties are mutually …

Recent advances in machine learning methods have created opportunities to eliminate
unfairness from algorithmic decision making. Multiple computational techniques (ie …