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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 …
The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Data collection and quality challenges in deep learning: A data-centric ai perspective
Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …
learning becomes the new software, powered by big data and computing infrastructure …
Lift: Language-interfaced fine-tuning for non-language machine learning tasks
Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …
has become a norm for learning various language downstream tasks. However, for non …
Fairfed: Enabling group fairness in federated learning
Training ML models which are fair across different demographic groups is of critical
importance due to the increased integration of ML in crucial decision-making scenarios such …
importance due to the increased integration of ML in crucial decision-making scenarios such …
In-processing modeling techniques for machine learning fairness: A survey
Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …
clear benefits in terms of performance, the models could show discrimination against …
Fairness without demographics through knowledge distillation
Most of existing work on fairness assumes available demographic information in the training
set. In practice, due to legal or privacy concerns, when demographic information is not …
set. In practice, due to legal or privacy concerns, when demographic information is not …
Sample selection for fair and robust training
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …
together. Fairness is about learning an unbiased model while robustness is about learning …
Improving fairness via federated learning
Recently, lots of algorithms have been proposed for learning a fair classifier from
decentralized data. However, many theoretical and algorithmic questions remain open. First …
decentralized data. However, many theoretical and algorithmic questions remain open. First …
Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …
decisions, directly impacting people's well-being. As a result, a growing community of …