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Found 5 papers, 1 papers with code
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
Causal discovery (CD) plays a pivotal role in numerous scientific fields by clarifying the causal relationships that underlie phenomena observed in diverse disciplines.
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
Bias originates from both data and algorithmic design, often exacerbated by traditional fairness methods that fail to address the subtle impacts of protected attributes.
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis.
Bias Reducing Multitask Learning on Mental Health Prediction
However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities.
Studying the Impact of Mood on Identifying Smartphone Users
This paper explores the identification of smartphone users when certain samples collected while the subject felt happy, upset or stressed were absent or present.
