Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Shortcut learning in deep neural networks
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of
today's machine intelligence. Numerous success stories have rapidly spread all over …
today's machine intelligence. Numerous success stories have rapidly spread all over …
Bias mitigation for machine learning classifiers: A comprehensive survey
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
A survey on bias and fairness in machine learning
With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …
everyday lives, accounting for fairness has gained significant importance in designing and …
Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Graph neural networks (GNNs) have shown great power in modeling graph structured data.
However, similar to other machine learning models, GNNs may make predictions biased on …
However, similar to other machine learning models, GNNs may make predictions biased on …
Minimax pareto fairness: A multi objective perspective
In this work we formulate and formally characterize group fairness as a multi-objective
optimization problem, where each sensitive group risk is a separate objective. We propose a …
optimization problem, where each sensitive group risk is a separate objective. We propose a …
Towards fairness in visual recognition: Effective strategies for bias mitigation
Computer vision models learn to perform a task by capturing relevant statistics from training
data. It has been shown that models learn spurious age, gender, and race correlations when …
data. It has been shown that models learn spurious age, gender, and race correlations when …
Weakly-supervised disentanglement without compromises
Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …
their environment. We model such observations as pairs of non-iid images sharing at least …
Learning de-biased representations with biased representations
Many machine learning algorithms are trained and evaluated by splitting data from a single
source into training and test sets. While such focus on in-distribution learning scenarios has …
source into training and test sets. While such focus on in-distribution learning scenarios has …
[HTML][HTML] Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …
using deep learning to advance discovery in medical imaging studies. Confounders affect …