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 …
A review on fairness in machine learning
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
Six human-centered artificial intelligence grand challenges
Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the
human condition in ways that are not yet well understood. Negative unintended …
human condition in ways that are not yet well understood. Negative unintended …
A comprehensive survey on design and application of autoencoder in deep learning
Autoencoder is an unsupervised learning model, which can automatically learn data
features from a large number of samples and can act as a dimensionality reduction method …
features from a large number of samples and can act as a dimensionality reduction method …
Generative adversarial networks: A survey toward private and secure applications
Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …
computer vision and natural language processing, among others, due to its generative …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
Environment inference for invariant learning
E Creager, JH Jacobsen… - … Conference on Machine …, 2021 - proceedings.mlr.press
Learning models that gracefully handle distribution shifts is central to research on domain
generalization, robust optimization, and fairness. A promising formulation is domain …
generalization, robust optimization, and fairness. A promising formulation is domain …
[引用][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Explainable deep learning: A field guide for the uninitiated
Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …
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 …