Cover Image

Distribution-Specific Auditing For Subgroup Fairness

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hsu, Daniel, Huang, Jizhou, Juba, Brendan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Complexity Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Distribution-Specific Agnostic Conditional Classification With Halfspaces
by: Huang, Jizhou, et al.
Published: (2025)

Marginal Fairness: Fair Decision-Making under Risk Measures
by: Huang, Fei, et al.
Published: (2025)

Hardness of Maximum Likelihood Learning of DPPs
by: Grigorescu, Elena, et al.
Published: (2022)

FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups
by: Giannopoulos, Giorgos, et al.
Published: (2024)

Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distribution
by: Huang, Jizhou, et al.
Published: (2025)

Generalizing Fair Top-$k$ Selection: An Integrative Approach
by: Cai, Guangya
Published: (2026)

From Pseudorandomness to Multi-Group Fairness and Back
by: Dwork, Cynthia, et al.
Published: (2023)

The Alignment Trap: Complexity Barriers
by: Yao, Jasper
Published: (2025)

Learning Linear Utility Functions From Pairwise Comparison Queries
by: Ge, Luise, et al.
Published: (2024)

Near-Optimal Cryptographic Hardness of Learning With Homogeneous Halfspaces Under Gaussian Marginals
by: Huang, Jizhou, et al.
Published: (2026)

Auditing Fairness under Unobserved Confounding
by: Byun, Yewon, et al.
Published: (2024)

Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
by: Hartmann, David, et al.
Published: (2026)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)

Auditing LLMs for Algorithmic Fairness in Casenote-Augmented Tabular Prediction
by: Lee, Xiao Qi, et al.
Published: (2026)

Auditing the Fairness of the US COVID-19 Forecast Hub's Case Prediction Models
by: Abrar, Saad Mohammad, et al.
Published: (2024)

Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
by: Zhou, Kacy, et al.
Published: (2024)

Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers
by: Amiri, Alireza, et al.
Published: (2025)

Auditing Fairness by Betting
by: Chugg, Ben, et al.
Published: (2023)

Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)

On the Hardness of Approximation of the Fair k-Center Problem
by: Thejaswi, Suhas
Published: (2026)

Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation
by: Amir, Guy, et al.
Published: (2024)

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups
by: Popoola, Gideon, et al.
Published: (2026)

Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing
by: Fang, Shiqi, et al.
Published: (2024)

An Explainable and Fair AI Tool for PCOS Risk Assessment: Calibration, Subgroup Equity, and Interactive Clinical Deployment
by: Khan, Asma Sadia, et al.
Published: (2025)

Cost Efficient Fairness Audit Under Partial Feedback
by: Das, Nirjhar, et al.
Published: (2025)

Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
by: Yuan, Chih-Cheng Rex, et al.
Published: (2025)

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?
by: Chen, Yuansi, et al.
Published: (2023)

Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
by: Basu, Debabrota, et al.
Published: (2025)

AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)

Learnability of Parameter-Bounded Bayes Nets
by: Bhattacharyya, Arnab, et al.
Published: (2024)

Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
by: Wang, Jie, et al.
Published: (2024)

Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension
by: Chandrasekaran, Gautam, et al.
Published: (2024)

Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
by: Li, Zhiyuan, et al.
Published: (2024)

Ask, and it shall be given: On the Turing completeness of prompting
by: Qiu, Ruizhong, et al.
Published: (2024)

Large Language Models on Small Resource-Constrained Systems: Performance Characterization, Analysis and Trade-offs
by: Seymour, Liam, et al.
Published: (2024)

Data Debugging is NP-hard for Classifiers Trained with SGD
by: Guo, Zizheng, et al.
Published: (2024)

On the Hardness of Learning Regular Expressions
by: Attias, Idan, et al.
Published: (2025)

A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
by: Merrill, William, et al.
Published: (2025)

How Hard Is Continuous Clustering? Lower Bounds from the Existential Theory of the Reals
by: Majumdar, Angshul
Published: (2026)

Spiky Rank and Its Applications to Rigidity and Circuits
by: Hambardzumyan, Lianna, et al.
Published: (2026)