arXiv Machine Learning
Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 9
Generalizing Fair Top-$k$ Selection: An Integrative Approach
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
Hugging Face Trending Papers
Aug 10
CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis
Fairness evaluation in computer vision commonly relies on aggregate accuracy and demographic subgroup analysis. However, visual models are also sensitive to contextual factors such as illumination, blur, image quality, facial accessories, and appearance attributes.
arXiv Machine Learning
Jun 19
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
arXiv Machine Learning
Jun 4
Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions
arXiv:2004. 10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT).
arXiv Machine Learning
Aug 12
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
arXiv:2605. 28626v2 Announce Type: replace Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter.
arXiv AI
Jul 9
SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps
arXiv:2602. 18201v2 Announce Type: replace Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training.