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 …
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 the fairness of recommender systems
Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …
and play an important role in people's daily lives. Since recommendations involve …
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 …
Learning from noisy labels with deep neural networks: A survey
Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …
amounts of big data. However, the quality of data labels is a concern because of the lack of …
Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …
purposes that include understanding, improving, and contesting predictions. Until now, most …
Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
No subclass left behind: Fine-grained robustness in coarse-grained classification problems
In real-world classification tasks, each class often comprises multiple finer-grained"
subclasses." As the subclass labels are frequently unavailable, models trained using only …
subclasses." As the subclass labels are frequently unavailable, models trained using only …
Large-scale methods for distributionally robust optimization
We propose and analyze algorithms for distributionally robust optimization of convex losses
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical …
L Belenguer - AI and Ethics, 2022 - Springer
A new and unorthodox approach to deal with discriminatory bias in Artificial Intelligence is
needed. As it is explored in detail, the current literature is a dichotomy with studies …
needed. As it is explored in detail, the current literature is a dichotomy with studies …