:: Library Catalog

Saved in:

Bibliographic Details
Main Authors:	Curti, Christian Beron, Sainz, Rodrigo Vargas, Tseo, Yitong
Format:	Preprint
Published:	2025
Subjects:	Computers and Society Machine Learning
Online Access:	https://arxiv.org/abs/2504.01045
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Identifying Climate Targets in National Laws and Policies using Machine Learning
by: Juhasz, Matyas, et al.
Published: (2024)

Insights From Insurance for Fair Machine Learning
by: Fröhlich, Christian, et al.
Published: (2023)

Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines
by: Rony, Md Main Uddin, et al.
Published: (2024)

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
by: Tertulino, Rodrigo, et al.
Published: (2025)

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach
by: Ahmed, Md Sabbir, et al.
Published: (2025)

Identifying Cyberbullying Roles in Social Media
by: Sandoval, Manuel, et al.
Published: (2024)

MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
by: Tkáč, Michal, et al.
Published: (2024)

Democratizing AI Governance: Balancing Expertise and Public Participation
by: Ter-Minassian, Lucile
Published: (2025)

Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdata
by: Tertulino, Rodrigo
Published: (2025)

Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)

Personalized Programming Guidance based on Deep Programming Learning Style Capturing
by: Liu, Yingfan, et al.
Published: (2024)

Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation
by: Peter, Oriane, et al.
Published: (2025)

Identifying Privacy Personas
by: Hrynenko, Olena, et al.
Published: (2024)

SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks
by: He, Enbo, et al.
Published: (2024)

The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning
by: Kraft, Angelie, et al.
Published: (2022)

Bias and Identifiability in the Bounded Confidence Model
by: Borile, Claudio, et al.
Published: (2025)

Non-Determinism and the Lawlessness of Machine Learning Code
by: Cooper, A. Feder, et al.
Published: (2022)

The Value of Prediction in Identifying the Worst-Off
by: Fischer-Abaigar, Unai, et al.
Published: (2025)

Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events
by: Coppitters, Diederik, et al.
Published: (2025)

Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
by: Wang, Angelina
Published: (2025)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)

Understanding Disparities in Post Hoc Machine Learning Explanation
by: Mhasawade, Vishwali, et al.
Published: (2024)

Best Practices for Responsible Machine Learning in Credit Scoring
by: Valdrighi, Giovani, et al.
Published: (2024)

What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)

An Ensemble Framework for Explainable Geospatial Machine Learning Models
by: Liu, Lingbo
Published: (2024)

A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting
by: Agyemang, Edmund F., et al.
Published: (2025)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
by: Kim, Woojin, et al.
Published: (2025)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
by: Anderson, Joshua Wolff, et al.
Published: (2025)

Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
by: Grand-Clément, Julien, et al.
Published: (2021)

Machine Learning Algorithms for Detecting Mental Stress in College Students
by: Singh, Ashutosh, et al.
Published: (2024)

AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)

Towards Public Administration Research Based on Interpretable Machine Learning
by: Liu, Zhanyu, et al.
Published: (2026)

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
by: Fawkes, Jake, et al.
Published: (2024)

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data
by: Tertulino, Rodrigo
Published: (2025)

FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
by: Souligne, Nick, et al.
Published: (2026)

SoK: Machine Learning for Misinformation Detection
by: Xiao, Madelyne, et al.
Published: (2023)

Emoji Promotes Developer Participation and Issue Resolution on GitHub
by: Zhou, Yuhang, et al.
Published: (2023)

Predicting and Understanding College Student Mental Health with Interpretable Machine Learning
by: Chowdhury, Meghna Roy, et al.
Published: (2025)

Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction
by: Chen, Yawen, et al.
Published: (2025)