Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study
Fuente:
arXiv
Saved in:
| Main Authors: | Valentine, Alissa A., Charney, Alexander W., Landi, Isotta |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
by: Valentine, Alissa A., et al.
Published: (2024)
by: Valentine, Alissa A., et al.
Published: (2024)
Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities
by: Valentine, Alissa A., et al.
Published: (2025)
by: Valentine, Alissa A., et al.
Published: (2025)
Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)
Fair Machine Learning in Healthcare: A Review
by: Feng, Qizhang, et al.
Published: (2022)
by: Feng, Qizhang, et al.
Published: (2022)
FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
by: Souligne, Nick, et al.
Published: (2026)
by: Souligne, Nick, et al.
Published: (2026)
Fairness in KI-Systemen
by: Strotherm, Janine, et al.
Published: (2023)
by: Strotherm, Janine, et al.
Published: (2023)
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)
by: Liu, Mingxuan, et al.
Published: (2024)
Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
by: Amin, Mostafa M., et al.
Published: (2022)
by: Amin, Mostafa M., et al.
Published: (2022)
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
by: Asiedu, Mercy Nyamewaa, et al.
Published: (2023)
by: Asiedu, Mercy Nyamewaa, et al.
Published: (2023)
What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)
by: Gao, Jianhui, et al.
Published: (2024)
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
by: Li, Siqi, et al.
Published: (2024)
by: Li, Siqi, et al.
Published: (2024)
An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems
by: Thu, Huyen Giang Thi, et al.
Published: (2024)
by: Thu, Huyen Giang Thi, et al.
Published: (2024)
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)
by: Huang, Yinghui, et al.
Published: (2024)
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
by: Fawkes, Jake, et al.
Published: (2024)
by: Fawkes, Jake, et al.
Published: (2024)
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)
by: Wang, Ziming, et al.
Published: (2025)
Intersectional Two-sided Fairness in Recommendation
by: Wang, Yifan, et al.
Published: (2024)
by: Wang, Yifan, et al.
Published: (2024)
A Tutorial On Intersectionality in Fair Rankings
by: Criscuolo, Chiara, et al.
Published: (2025)
by: Criscuolo, Chiara, et al.
Published: (2025)
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
by: Scantamburlo, Teresa, et al.
Published: (2023)
by: Scantamburlo, Teresa, et al.
Published: (2023)
Insights From Insurance for Fair Machine Learning
by: Fröhlich, Christian, et al.
Published: (2023)
by: Fröhlich, Christian, et al.
Published: (2023)
Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
by: Wang, Xiaoyang, et al.
Published: (2025)
by: Wang, Xiaoyang, et al.
Published: (2025)
Tuning Derivatives for Causal Fairness in Machine Learning
by: Edström, Filip, et al.
Published: (2026)
by: Edström, Filip, et al.
Published: (2026)
Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
by: Wang, Angelina
Published: (2025)
by: Wang, Angelina
Published: (2025)
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications
by: Agarwal, Ankita, et al.
Published: (2024)
by: Agarwal, Ankita, et al.
Published: (2024)
Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
by: Kim, Woojin, et al.
Published: (2025)
by: Kim, Woojin, et al.
Published: (2025)
The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
by: Anderson, Joshua Wolff, et al.
Published: (2025)
by: Anderson, Joshua Wolff, et al.
Published: (2025)
Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach
by: Wang, Xiaoyang, et al.
Published: (2025)
by: Wang, Xiaoyang, et al.
Published: (2025)
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
by: Shome, Arumoy, et al.
Published: (2024)
by: Shome, Arumoy, et al.
Published: (2024)
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
by: Bothmann, Ludwig, et al.
Published: (2023)
by: Bothmann, Ludwig, et al.
Published: (2023)
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
by: Gajane, Pratik, et al.
Published: (2024)
by: Gajane, Pratik, et al.
Published: (2024)
Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
by: Wolfrath, Nathan, et al.
Published: (2024)
by: Wolfrath, Nathan, et al.
Published: (2024)
On The Fairness Impacts of Hardware Selection in Machine Learning
by: Nelaturu, Sree Harsha, et al.
Published: (2023)
by: Nelaturu, Sree Harsha, et al.
Published: (2023)
Fair CCA for Fair Representation Learning: An ADNI Study
by: Hou, Bojian, et al.
Published: (2025)
by: Hou, Bojian, et al.
Published: (2025)
Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
by: Benítez-Peña, Sandra, et al.
Published: (2026)
by: Benítez-Peña, Sandra, et al.
Published: (2026)
MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups
by: Popoola, Gideon, et al.
Published: (2026)
by: Popoola, Gideon, et al.
Published: (2026)
A Fair Loss Function for Network Pruning
by: Meyer, Robbie, et al.
Published: (2022)
by: Meyer, Robbie, et al.
Published: (2022)
BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
by: Cohen-Inger, Nurit, et al.
Published: (2025)
by: Cohen-Inger, Nurit, et al.
Published: (2025)
Intersectional Unfairness Discovery
by: Xu, Gezheng, et al.
Published: (2024)
by: Xu, Gezheng, et al.
Published: (2024)
Mixture of Experts for Recognizing Depression from Interview and Reading Tasks
by: Ilias, Loukas, et al.
Published: (2025)
by: Ilias, Loukas, et al.
Published: (2025)
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
by: Zanna, Khadija, et al.
Published: (2024)
by: Zanna, Khadija, et al.
Published: (2024)
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
by: Somerstep, Seamus, et al.
Published: (2024)
by: Somerstep, Seamus, et al.
Published: (2024)
Similar Items
-
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
by: Valentine, Alissa A., et al.
Published: (2024) -
Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities
by: Valentine, Alissa A., et al.
Published: (2025) -
Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024) -
Fair Machine Learning in Healthcare: A Review
by: Feng, Qizhang, et al.
Published: (2022) -
FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
by: Souligne, Nick, et al.
Published: (2026)