Work

MultiFair: Model Fairness With Multiple Sensitive Attributes

Year: 2024

Type: article

Source: IEEE Transactions on Neural Networks and Learning Systems

DOI: 10.1109/tnnls.2024.3384181

Authors: Huan Tian, Bo Liu, Tianqing Zhu, Wanlei Zhou, Philip S. Yu

Institutions: University of Technology Sydney, City University of Macau, University of Illinois Chicago

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: closed

Funders: National Science Foundation, Australian Research Council

Awards: Deep Learning Attacks and Active Defences: A Cybersecurity Perspective, NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for Anomaly Detection, III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption, Balance and reinforcement: privacy and fairness in high intelligence models

OpenAlex ID: W4394994643

FWCI: 5.412

Cites: 49

Cited by: 13

Related to: 10