ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2407.03133
  4. Cited By
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness

Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness

24 May 2024
Yingfang Yuan
Kefan Chen
Mehdi Rizvi
Lynne Baillie
Wei Pang
ArXivPDFHTML

Papers citing "Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness"

2 / 2 papers shown
Title
Experts' View on Challenges and Needs for Fairness in Artificial
  Intelligence for Education
Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education
Gianni Fenu
Roberta Galici
Mirko Marras
38
17
0
23 Jun 2022
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
326
4,212
0
23 Aug 2019
1