No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models
Fuente:
arXiv
Saved in:
| Main Authors: | Legast, Magali, Calders, Toon, Fouss, François |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
How to be fair? A study of label and selection bias
by: Favier, Marco, et al.
Published: (2024)
by: Favier, Marco, et al.
Published: (2024)
Reranking individuals: The effect of fair classification within-groups
by: Goethals, Sofie, et al.
Published: (2024)
by: Goethals, Sofie, et al.
Published: (2024)
"Patriarchy Hurts Men Too." Does Your Model Agree? A Discussion on Fairness Assumptions
by: Favier, Marco, et al.
Published: (2024)
by: Favier, Marco, et al.
Published: (2024)
Cherry on the Cake: Fairness is NOT an Optimization Problem
by: Favier, Marco, et al.
Published: (2024)
by: Favier, Marco, et al.
Published: (2024)
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness
by: Krco, Natasa, et al.
Published: (2023)
by: Krco, Natasa, et al.
Published: (2023)
Hierarchical confusion matrix for classification performance evaluation
by: Riehl, Kevin, et al.
Published: (2023)
by: Riehl, Kevin, et al.
Published: (2023)
MLMC: Interactive multi-label multi-classifier evaluation without confusion matrices
by: Doknic, Aleksandar, et al.
Published: (2025)
by: Doknic, Aleksandar, et al.
Published: (2025)
SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models
by: Rossi, Alvise Dei, et al.
Published: (2025)
by: Rossi, Alvise Dei, et al.
Published: (2025)
Multiclass threshold-based classification and model evaluation
by: Legnaro, Edoardo, et al.
Published: (2025)
by: Legnaro, Edoardo, et al.
Published: (2025)
Simulating classification models to evaluate Predict-Then-Optimize methods
by: Smet, Pieter
Published: (2025)
by: Smet, Pieter
Published: (2025)
Understanding and mitigating difficulties in posterior predictive evaluation
by: Agrawal, Abhinav, et al.
Published: (2024)
by: Agrawal, Abhinav, et al.
Published: (2024)
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
by: Pfohl, Stephen R., et al.
Published: (2025)
by: Pfohl, Stephen R., et al.
Published: (2025)
Interpretable and Fair Mechanisms for Abstaining Classifiers
by: Lenders, Daphne, et al.
Published: (2025)
by: Lenders, Daphne, et al.
Published: (2025)
Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images
by: Vlontzou, Maria Eleftheria, et al.
Published: (2025)
by: Vlontzou, Maria Eleftheria, et al.
Published: (2025)
Explainable post-training bias mitigation with distribution-based fairness metrics
by: Franks, Ryan, et al.
Published: (2025)
by: Franks, Ryan, et al.
Published: (2025)
Fairmetrics: An R package for group fairness evaluation
by: Smith, Benjamin, et al.
Published: (2025)
by: Smith, Benjamin, et al.
Published: (2025)
A metrological framework for uncertainty evaluation in machine learning classification models
by: Bilson, Samuel, et al.
Published: (2025)
by: Bilson, Samuel, et al.
Published: (2025)
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
by: Semenova, Nadezhda, et al.
Published: (2024)
by: Semenova, Nadezhda, et al.
Published: (2024)
Generalized test utilities for long-tail performance in extreme multi-label classification
by: Schultheis, Erik, et al.
Published: (2023)
by: Schultheis, Erik, et al.
Published: (2023)
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation
by: Goto, Isao
Published: (2024)
by: Goto, Isao
Published: (2024)
Imputation using training labels and classification via label imputation
by: Nguyen, Thu, et al.
Published: (2023)
by: Nguyen, Thu, et al.
Published: (2023)
Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation
by: Brown, David H., et al.
Published: (2024)
by: Brown, David H., et al.
Published: (2024)
A unified weighting framework for evaluating nearest neighbour classification
by: Lenz, Oliver Urs, et al.
Published: (2023)
by: Lenz, Oliver Urs, et al.
Published: (2023)
Are demographically invariant models and representations in medical imaging fair?
by: Petersen, Eike, et al.
Published: (2023)
by: Petersen, Eike, et al.
Published: (2023)
BiMi Sheets: Infosheets for bias mitigation methods
by: Defrance, MaryBeth, et al.
Published: (2025)
by: Defrance, MaryBeth, et al.
Published: (2025)
Latent label distribution grid representation for modeling uncertainty
by: Sun, ShuNing, et al.
Published: (2025)
by: Sun, ShuNing, et al.
Published: (2025)
Federated fairness-aware classification under differential privacy
by: Xue, Gengyu, et al.
Published: (2026)
by: Xue, Gengyu, et al.
Published: (2026)
Detecting labeling bias using influence functions
by: Jørgensen, Frida, et al.
Published: (2026)
by: Jørgensen, Frida, et al.
Published: (2026)
Effects of label noise on the classification of outlier observations
by: de Farias, Matheus Vinícius Barreto, et al.
Published: (2025)
by: de Farias, Matheus Vinícius Barreto, et al.
Published: (2025)
Active learning with biased non-response to label requests
by: Robinson, Thomas, et al.
Published: (2023)
by: Robinson, Thomas, et al.
Published: (2023)
Deep adaptive sampling for surrogate modeling without labeled data
by: Wang, Xili, et al.
Published: (2024)
by: Wang, Xili, et al.
Published: (2024)
Investigation into using stochastic embedding representations for evaluating the trustworthiness of the Fréchet Inception Distance
by: Bench, Ciaran, et al.
Published: (2026)
by: Bench, Ciaran, et al.
Published: (2026)
Bilinear representation mitigates reversal curse and enables consistent model editing
by: Kim, Dong-Kyum, et al.
Published: (2025)
by: Kim, Dong-Kyum, et al.
Published: (2025)
Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
by: Khodakarami, Siavash, et al.
Published: (2026)
by: Khodakarami, Siavash, et al.
Published: (2026)
PLS-based approach for fair representation learning
by: De-Diego, Elena M., et al.
Published: (2025)
by: De-Diego, Elena M., et al.
Published: (2025)
Model-agnostic clean-label backdoor mitigation in cybersecurity environments
by: Severi, Giorgio, et al.
Published: (2024)
by: Severi, Giorgio, et al.
Published: (2024)
No imputation without representation
by: Lenz, Oliver Urs, et al.
Published: (2022)
by: Lenz, Oliver Urs, et al.
Published: (2022)
A structured regression approach for evaluating model performance across intersectional subgroups
by: Herlihy, Christine, et al.
Published: (2024)
by: Herlihy, Christine, et al.
Published: (2024)
A model-free subdata selection method for classification
by: Singh, Rakhi
Published: (2024)
by: Singh, Rakhi
Published: (2024)
Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics
by: Bhattacharyya, Swarnava, et al.
Published: (2025)
by: Bhattacharyya, Swarnava, et al.
Published: (2025)
Similar Items
-
How to be fair? A study of label and selection bias
by: Favier, Marco, et al.
Published: (2024) -
Reranking individuals: The effect of fair classification within-groups
by: Goethals, Sofie, et al.
Published: (2024) -
"Patriarchy Hurts Men Too." Does Your Model Agree? A Discussion on Fairness Assumptions
by: Favier, Marco, et al.
Published: (2024) -
Cherry on the Cake: Fairness is NOT an Optimization Problem
by: Favier, Marco, et al.
Published: (2024) -
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness
by: Krco, Natasa, et al.
Published: (2023)