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 …
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 …
Generative adversarial networks (GANs) challenges, solutions, and future directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …
has recently gained significant attention. GANs learn complex and high-dimensional …
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 …
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 …
Fairness in deep learning: A computational perspective
Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …
increasingly being used in high-stake decision making applications that affect individual …
Algorithmic fairness
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …
Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization
Deep learning has achieved tremendous success with independent and identically
distributed (iid) data. However, the performance of neural networks often degenerates …
distributed (iid) data. However, the performance of neural networks often degenerates …
Fairness-aware adversarial perturbation towards bias mitigation for deployed deep models
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially
for those societal applications, eg, hiring systems should recommend applicants equally …
for those societal applications, eg, hiring systems should recommend applicants equally …
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and …
The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …