Cargando…

A Maximal Correlation Framework for Fair Machine Learning

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is introd...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lee, Joshua, Bu, Yuheng, Sattigeri, Prasanna, Panda, Rameswar, Wornell, Gregory W., Karlinsky, Leonid, Schmidt Feris, Rogerio
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9027582/ https://www.ncbi.nlm.nih.gov/pubmed/35455124 http://dx.doi.org/10.3390/e24040461

Ejemplares similares

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes
por: Galhotra, Sainyam, et al.
Publicado: (2021)

Differential Fairness: An Intersectional Framework for Fair AI †
por: Islam, Rashidul, et al.
Publicado: (2023)

A United States Fair Lending Perspective on Machine Learning
por: Hall, Patrick, et al.
Publicado: (2021)

Signal processing with fractals: a wavelet-based approach
por: Wornell, Gregory
Publicado: (1996)

Enabling Fairness in Healthcare Through Machine Learning
por: Grote, Thomas, et al.
Publicado: (2022)

Maximizing multiple influences and fair seed allocation on multilayer social networks
por: Chen, Yu, et al.
Publicado: (2020)

Fair Max–Min Diversity Maximization in Streaming and Sliding-Window Models †
por: Wang, Yanhao, et al.
Publicado: (2023)

A General Framework for Fair Regression
por: Fitzsimons, Jack, et al.
Publicado: (2019)

A framework for FAIR robotic datasets
por: Motta, Corrado, et al.
Publicado: (2023)

Covid-19 and age discrimination: benefit maximization, fairness, and justified age-based rationing
por: Albertsen, Andreas
Publicado: (2022)

ALFA: The new ALICE-FAIR software framework
por: Al-Turany, M, et al.
Publicado: (2015)

Stochastic Cooling in the Framework of the FAIR Project at GSI
por: Nolden, F, et al.
Publicado: (2008)

A design framework and exemplar metrics for FAIRness
por: Wilkinson, Mark D., et al.
Publicado: (2018)

SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development
por: Curto, Georgina, et al.
Publicado: (2023)

Fairness-aware machine learning engineering: how far are we?
por: Ferrara, Carmine, et al.
Publicado: (2023)

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression
por: Yuan, Chenxi, et al.
Publicado: (2023)

Discriminative machine learning for maximal representative subsampling
por: Hauptmann, Tony, et al.
Publicado: (2023)

Equity in essence: a call for operationalising fairness in machine learning for healthcare
por: Wawira Gichoya, Judy, et al.
Publicado: (2021)

On the relationship between research parasites and fairness in machine learning: challenges and opportunities
por: Nieto, Nicolás, et al.
Publicado: (2021)

Fairness in the prediction of acute postoperative pain using machine learning models
por: Davoudi, Anis, et al.
Publicado: (2023)

Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
por: Yang, Jenny, et al.
Publicado: (2023)

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics
por: Levinson, Maxwell Adam, et al.
Publicado: (2021)

FAIR in action - a flexible framework to guide FAIRification
por: Welter, Danielle, et al.
Publicado: (2023)

Neural population partitioning and a concurrent brain-machine interface for sequential motor function
por: Shanechi, Maryam M., et al.
Publicado: (2012)

Addressing vulnerability and exclusion in the South African small-scale fisheries sector: does the current regulatory framework measure up?
por: Auld, Kathleen, et al.
Publicado: (2022)

Maximizing reusability of learning objects through machine learning techniques
por: Amane, Meryem, et al.
Publicado: (2023)

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics
por: Qi, Miao, et al.
Publicado: (2021)

Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning
por: Papalexopoulos, Theodore P, et al.
Publicado: (2022)

Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults
por: Schuch, Helena Silveira, et al.
Publicado: (2023)

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria
por: Roux, Simon, et al.
Publicado: (2023)

Event Collapse in Contrast Maximization Frameworks
por: Shiba, Shintaro, et al.
Publicado: (2022)

Exploring polyglot software frameworks in ALICE with FairMQ and fer
por: Binet, S.
Publicado: (2019)

Catuai Amarillo's experience in the framework of organic production and fair trade
por: Escamilla Prado, Esteban
Publicado: (2021)

The H3Africa policy framework: negotiating fairness in genomics
por: de Vries, Jantina, et al.
Publicado: (2015)

Fair Healthcare Practices in Orthopedics Assessed with a New Framework
por: Moldovan, Flaviu, et al.
Publicado: (2023)

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design
por: Shanechi, Maryam M., et al.
Publicado: (2013)

Fairness for Transgender People in Sport
por: Safer, Joshua D
Publicado: (2022)

An introduction to machine learning interpretability: an applied perspective on fairness, accountability, transparency, and explainable AI
por: Hall, Patrick, et al.
Publicado: (2019)

An introduction to machine learning interpretability: an applied perspective on fairness, accountability, transparency, and explainable AI
por: Hall, Patrick, et al.
Publicado: (2018)

Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
por: Choraś, Michał, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_iahoen79ifbck53a9fpkfb2v1k