Racial bias in clinical and population health algorithms: a critical review of current debates
Among health care researchers, there is increasing debate over how best to assess and
ensure the fairness of algorithms used for clinical decision support and population health …
ensure the fairness of algorithms used for clinical decision support and population health …
Develo** medical imaging AI for emerging infectious diseases
Advances in artificial intelligence (AI) and computer vision hold great promise for assisting
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …
Characterizing the clinical adoption of medical AI devices through US insurance claims
There are now over 500 medical artificial intelligence (AI) devices that are approved by the
US Food and Drug Administration. However, little is known about where and how often …
US Food and Drug Administration. However, little is known about where and how often …
The measure and mismeasure of fairness
The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
Causal conceptions of fairness and their consequences
Recent work highlights the role of causality in designing equitable decision-making
algorithms. It is not immediately clear, however, how existing causal conceptions of fairness …
algorithms. It is not immediately clear, however, how existing causal conceptions of fairness …
A nationwide network of health AI assurance laboratories
Importance Given the importance of rigorous development and evaluation standards
needed of artificial intelligence (AI) models used in health care, nationwide accepted …
needed of artificial intelligence (AI) models used in health care, nationwide accepted …
Evaluation gaps in machine learning practice
Forming a reliable judgement of a machine learning (ML) model's appropriateness for an
application ecosystem is critical for its responsible use, and requires considering a broad …
application ecosystem is critical for its responsible use, and requires considering a broad …
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Inappropriate design and deployment of machine learning (ML) systems lead to negative
downstream social and ethical impacts–described here as social and ethical risks–for users …
downstream social and ethical impacts–described here as social and ethical risks–for users …
Optimal and fair encouragement policy evaluation and learning
A Zhou - Advances in Neural Information Processing …, 2024 - proceedings.neurips.cc
In consequential domains, it is often impossible to compel individuals to take treatment, so
that optimal policy rules are merely suggestions in the presence of human non-adherence to …
that optimal policy rules are merely suggestions in the presence of human non-adherence to …
Algorithmic bias in criminal risk assessment: the consequences of racial differences in arrest as a measure of crime
There is great concern about algorithmic racial bias in the risk assessment instruments
(RAIs) used in the criminal legal system. When testing for algorithmic bias, most research …
(RAIs) used in the criminal legal system. When testing for algorithmic bias, most research …