Analyzing and Addressing Data-driven Fairness Issues in Machine Learning Models used for Societal Problems-Reference-Cited by-同舟云学术

Analyzing and Addressing Data-driven Fairness Issues in Machine Learning Models used for Societal Problems

Published:2023-01-20 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)
language:
Short-container-title:

Author:

Pendyala Vishnu S.¹,Kim HyungKyun²

Affiliation:

1. San Jose State University,Department of Applied Data Science,San Jose,United States

2. San Jose State University,Department of Computer Science,San Jose,United States

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10084914/10084930/10085470.pdf?arnumber=10085470

Reference26 articles.

1. Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for Predictive Tasks in Education

2. Two simple ways to learn individual fairness metrics from data;mukherjee;International Conference on Machine Learning,2020

3. Fairness without demographics through adversarially reweighted learning;lahoti;Advances in neural information processing systems,2020

4. Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes

5. Can i trust my fairness metric? assessing fairness with unlabeled data and bayesian inference;ji;Advances in neural information processing systems,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI;Electronics;2024-03-08

2. Analysis of Multilanguage Regional Music Tracks Using Representation Learning Techniques in Lower Dimensions;Lecture Notes in Networks and Systems;2024

3. Spectral analysis perspective of why misinformation containment is still an unsolved problem;2023 IEEE Conference on Artificial Intelligence (CAI);2023-06