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 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 …
Fairness in ranking, part i: Score-based ranking
In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …
algorithmic rankers, with contributions coming from the data management, algorithms …
Fairness without demographics through adversarially reweighted learning
Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …
features such as race and sex are present in the dataset, and relies upon them to mitigate …
OpenFL: the open federated learning library
P Foley, MJ Sheller, B Edwards, S Pati… - Physics in Medicine …, 2022 - iopscience.iop.org
Objective. Federated learning (FL) is a computational paradigm that enables organizations
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …
OpenFL: An open-source framework for Federated Learning
GA Reina, A Gruzdev, P Foley, O Perepelkina… - arXiv preprint arXiv …, 2021 - arxiv.org
Federated learning (FL) is a computational paradigm that enables organizations to
collaborate on machine learning (ML) projects without sharing sensitive data, such as …
collaborate on machine learning (ML) projects without sharing sensitive data, such as …
Fairness in ranking: A survey
In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …
algorithmic rankers, with contributions coming from the data management, algorithms …
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 …
Characterizing fairness over the set of good models under selective labels
A Coston, A Rambachan… - … on Machine Learning, 2021 - proceedings.mlr.press
Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes
settings. Often multiple predictive models deliver similar overall performance but differ …
settings. Often multiple predictive models deliver similar overall performance but differ …