Cover Image

Towards Public Administration Research Based on Interpretable Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Zhanyu, Yu, Yang
Format:	Preprint
Published:	2026
Subjects:	Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Observation Time Window Segmentation for Administrative Data Machine Learning
by: Taib, Musa, et al.
Published: (2024)

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

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

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)

Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety
by: Gupta, Sia, et al.
Published: (2024)

Learning Interpretable Fair Representations
by: Wang, Tianhao, et al.
Published: (2024)

Towards Explainable Evaluation Metrics for Machine Translation
by: Leiter, Christoph, et al.
Published: (2023)

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
by: Rodolfa, Kit T., et al.
Published: (2025)

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

Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning
by: Ma, Junwei, et al.
Published: (2024)

Putnam's Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective
by: Soudin, Sheldon Z.
Published: (2025)

Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning
by: Wang, Chun, et al.
Published: (2024)

Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
by: Wolfrath, Nathan, et al.
Published: (2024)

Building Socially-Equitable Public Models
by: Liu, Yejia, et al.
Published: (2024)

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

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

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
by: Wang, Shuangbao Paul, et al.
Published: (2025)

Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning
by: Liu, Ruying, et al.
Published: (2025)

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

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

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human-infrastructure Systems Performance
by: Li, Xiangpeng, et al.
Published: (2024)

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)

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

Estimating Item Difficulty Using Large Language Models and Tree-Based Machine Learning Algorithms
by: Razavi, Pooya, et al.
Published: (2025)

Fast and Interpretable Mortality Risk Scores for Critical Care Patients
by: Zhu, Chloe Qinyu, et al.
Published: (2023)

Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
by: Pilgram, Lisa, et al.
Published: (2025)

Towards Autonomous Mathematics Research
by: Feng, Tony, et al.
Published: (2026)

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)

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

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

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

Machine Learning for Identifying Potential Participants in Uruguayan Social Programs
by: Curti, Christian Beron, et al.
Published: (2025)

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

Detection of Problem Gambling with Less Features Using Machine Learning Methods
by: Jiao, Yang, et al.
Published: (2024)

Machine Learning-Based Cloud Computing Compliance Process Automation
by: Wang, Yuqing, et al.
Published: (2025)

Research on Individual Trait Clustering and Development Pathway Adaptation Based on the K-means Algorithm
by: Wei, Qianru, et al.
Published: (2026)