Cover Image

Fairness Improvement with Multiple Protected Attributes: How Far Are We?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Zhenpeng, Zhang, Jie M., Sarro, Federica, Harman, Mark
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Computers and Society Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
by: Chen, Zhenpeng, et al.
Published: (2024)

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
by: Li, Xinyue, et al.
Published: (2023)

Fairness Testing: A Comprehensive Survey and Analysis of Trends
by: Chen, Zhenpeng, et al.
Published: (2022)

On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
by: Moussa, Rebecca, et al.
Published: (2022)

Generative AI for Testing of Autonomous Driving Systems: A Survey
by: Song, Qunying, et al.
Published: (2025)

Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
by: Zhang, Dawen, et al.
Published: (2023)

Predicting Fairness of ML Software Configurations
by: Herrera, Salvador Robles, et al.
Published: (2024)

Fairness Testing through Extreme Value Theory
by: Monjezi, Verya, et al.
Published: (2025)

MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search
by: Wang, Zhaohui, et al.
Published: (2024)

Rethinking Technological Readiness in the Era of AI Uncertainty
by: Browne, S. Tucker, et al.
Published: (2025)

Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
by: Rajput, Saurabhsingh, et al.
Published: (2023)

Mutation-Guided LLM-based Test Generation at Meta
by: Foster, Christopher, et al.
Published: (2025)

Retrieval-Augmented Test Generation: How Far Are We?
by: Shin, Jiho, et al.
Published: (2024)

Language model developers should report train-test overlap
by: Zhang, Andy K, et al.
Published: (2024)

Optimizing Travel Itineraries with AI Algorithms in a Microservices Architecture: Balancing Cost, Time, Preferences, and Sustainability
by: Barua, Biman, et al.
Published: (2024)

NeuFair: Neural Network Fairness Repair with Dropout
by: Dasu, Vishnu Asutosh, et al.
Published: (2024)

LLM For Loop Invariant Generation and Fixing: How Far Are We?
by: Akhond, Mostafijur Rahman, et al.
Published: (2025)

How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias
by: Fadahunsi, Tosin, et al.
Published: (2025)

Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
by: Cheng, Chih-Hong, et al.
Published: (2024)

Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Project
by: Yim, Keun Soo
Published: (2024)

Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?
by: Wang, Taiming, et al.
Published: (2025)

NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims
by: Vishwarupe, Varad, et al.
Published: (2026)

An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping
by: Xia, Boming, et al.
Published: (2024)

Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
by: Raheem, Mira, et al.
Published: (2025)

A Conceptual Framework for Ethical Evaluation of Machine Learning Systems
by: Gupta, Neha R., et al.
Published: (2024)

Measuring Agents in Production
by: Pan, Melissa Z., et al.
Published: (2025)

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs
by: Sikand, Samarth, et al.
Published: (2025)

Green AI in Action: Strategic Model Selection for Ensembles in Production
by: Nijkamp, Nienke, et al.
Published: (2024)

The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
by: White, Matt, et al.
Published: (2024)

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
by: Seedat, Nabeel, et al.
Published: (2022)

Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
by: Tulili, Tien Rahayu, et al.
Published: (2026)

Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
by: Jewitt, James, et al.
Published: (2026)

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
by: Dascalescu, Stefan, et al.
Published: (2025)

Legal Aspects for Software Developers Interested in Generative AI Applications
by: Herbold, Steffen, et al.
Published: (2024)

Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
by: Sah, Chandan Kumar, et al.
Published: (2026)

SETA: Statistical Fault Attribution for Compound AI Systems
by: Chowdhury, Sayak, et al.
Published: (2026)

Using Quality Attribute Scenarios for ML Model Test Case Generation
by: Brower-Sinning, Rachel, et al.
Published: (2024)

ProToken: Token-Level Attribution for Federated Large Language Models
by: Gill, Waris, et al.
Published: (2026)

Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations
by: Akash, Ranit Debnath, et al.
Published: (2025)

Zero-Shot Attribution for Large Language Models: A Distribution Testing Approach
by: Canonne, Clément L., et al.
Published: (2025)