Copertina

Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Martins, Inês, Matos, João, Gonçalves, Tiago, Celi, Leo A., Wong, A. Ian, Cardoso, Jaime S.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams
di: Fernandes, Leonor, et al.
Pubblicazione: (2025)

Classification of Keratitis from Eye Corneal Photographs using Deep Learning
di: Beirão, Maria Miguel, et al.
Pubblicazione: (2024)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
di: Christodoulou, Leonidas, et al.
Pubblicazione: (2026)

Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
di: Briscoe, Jarren, et al.
Pubblicazione: (2025)

KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
di: Nnamdi, Micky C, et al.
Pubblicazione: (2026)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
di: Kim, Woojin, et al.
Pubblicazione: (2025)

Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learning
di: Root, Andrew, et al.
Pubblicazione: (2024)

Certifying Counterfactual Bias in LLMs
di: Chaudhary, Isha, et al.
Pubblicazione: (2024)

Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning
di: Mathew, Sheryl, et al.
Pubblicazione: (2025)

Generalization in medical AI: a perspective on developing scalable models
di: Zvuloni, Eran, et al.
Pubblicazione: (2023)

Better Think Thrice: Learning to Reason Causally with Double Counterfactual Consistency
di: Lin, Victoria, et al.
Pubblicazione: (2026)

Counterfactual Fairness through Transforming Data Orthogonal to Bias
di: Chen, Shuyi, et al.
Pubblicazione: (2024)

Sample Selection Bias in Machine Learning for Healthcare
di: Chauhan, Vinod Kumar, et al.
Pubblicazione: (2024)

Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
di: Xiang, Xiao, et al.
Pubblicazione: (2026)

Ensuring Equitable Financial Decisions: Leveraging Counterfactual Fairness and Deep Learning for Bias
di: Shinde, Saish
Pubblicazione: (2024)

Building Machines that Learn and Think with People
di: Collins, Katherine M., et al.
Pubblicazione: (2024)

Critical Appraisal of Fairness Metrics in Clinical Predictive AI
di: Matos, João, et al.
Pubblicazione: (2025)

Robust Counterfactual Explanations in Machine Learning: A Survey
di: Jiang, Junqi, et al.
Pubblicazione: (2024)

Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
di: Kohankhaki, Farnaz, et al.
Pubblicazione: (2024)

Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection
di: Zhang, Jiarui, et al.
Pubblicazione: (2024)

Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding
di: Barnatchez, Keith, et al.
Pubblicazione: (2026)

Budget-constrained Collaborative Renewable Energy Forecasting Market
di: Goncalves, Carla, et al.
Pubblicazione: (2025)

Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
di: Pires, Pedro R., et al.
Pubblicazione: (2025)

Conformal Counterfactual Inference under Hidden Confounding
di: Chen, Zonghao, et al.
Pubblicazione: (2024)

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
di: Pereira, Tomás, et al.
Pubblicazione: (2026)

Towards a Systematic Approach to Design New Ensemble Learning Algorithms
di: Mendes-Moreira, João, et al.
Pubblicazione: (2024)

Using Machine Bias To Measure Human Bias
di: Dong, Wanxue, et al.
Pubblicazione: (2024)

Debiasing Machine Unlearning with Counterfactual Examples
di: Chen, Ziheng, et al.
Pubblicazione: (2024)

Clinical Uncertainty Impacts Machine Learning Evaluations
di: Lionetti, Simone, et al.
Pubblicazione: (2025)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
di: Chhablani, Chirag, et al.
Pubblicazione: (2024)

Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias
di: Denton, Remi, et al.
Pubblicazione: (2019)

Pulse Shape Simulation and Discrimination using Machine-Learning Techniques
di: Dutta, Shubham, et al.
Pubblicazione: (2022)

Unsupervised Structural-Counterfactual Generation under Domain Shift
di: Kher, Krishn Vishwas, et al.
Pubblicazione: (2025)

Learning Counterfactually Invariant Predictors
di: Quinzan, Francesco, et al.
Pubblicazione: (2022)

Counterfactually Safe Reinforcement Learning
di: Li, Jingyi, et al.
Pubblicazione: (2026)

Bias Correction in Machine Learning-based Classification of Rare Events
di: Gubbels, Luuk, et al.
Pubblicazione: (2024)

AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
di: Ji, Xiayan, et al.
Pubblicazione: (2024)

Spectral Bias in Variational Quantum Machine Learning
di: Duffy, Callum, et al.
Pubblicazione: (2025)

Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning
di: Belahcen, Adam, et al.
Pubblicazione: (2026)

Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
di: Yang, Yongquan
Pubblicazione: (2026)