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 …
Pulse: Self-supervised photo upsampling via latent space exploration of generative models
The primary aim of single-image super-resolution is to construct a high-resolution (HR)
image from a corresponding low-resolution (LR) input. In previous approaches, which have …
image from a corresponding low-resolution (LR) input. In previous approaches, which have …
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 …
Tabfairgan: Fair tabular data generation with generative adversarial networks
With the increasing reliance on automated decision making, the issue of algorithmic fairness
has gained increasing importance. In this paper, we propose a Generative Adversarial …
has gained increasing importance. In this paper, we propose a Generative Adversarial …
Non-imaging medical data synthesis for trustworthy AI: A comprehensive survey
X **ng, H Wu, L Wang, I Stenson, M Yong… - ACM Computing …, 2024 - dl.acm.org
Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume
of curated datasets with controlled confounding factors can improve the accuracy …
of curated datasets with controlled confounding factors can improve the accuracy …
Fairness for image generation with uncertain sensitive attributes
This work tackles the issue of fairness in the context of generative procedures, such as
image super-resolution, which entail different definitions from the standard classification …
image super-resolution, which entail different definitions from the standard classification …
Fairness-aware machine learning engineering: how far are we?
Abstract Machine learning is part of the daily life of people and companies worldwide.
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …
Fair Bayes-optimal classifiers under predictive parity
Increasing concerns about disparate effects of AI have motivated a great deal of work on fair
machine learning. Existing works mainly focus on independence-and separation-based …
machine learning. Existing works mainly focus on independence-and separation-based …
An information theoretic approach to reducing algorithmic bias for machine learning
Algorithmic bias indicates the discrimination caused by algorithms, which occurs with
protected features such as gender and race. Many researchers have tried to define the …
protected features such as gender and race. Many researchers have tried to define the …
Learning fair graph neural networks with limited and private 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 biased predictions wrt …
However, similar to other machine learning models, GNNs may make biased predictions wrt …