Research

2026

1. Machine Learning

   Explainable AI

   Group Fairness

   Health Informatics

   Instance level analysis of equitable learning via dissimilar variable grouping on healthcare datasets

   Hyeonggeun Yun, Amanda Barnard^*, and Hanna Suominen^*

   International Journal of Medical Informatics, 2026

   DOI PDF Artefact
2. Machine Learning

   Individual Fairness

   Reliable Individual Fairness Evaluation through Coverage, Metric Agreement, and Model Stability

   Hyeonggeun Yun and Shahadat* Uddin

   In Australasian Joint Conference on Artificial Intelligence, 2026

   PDF Artefact

2025

1. Machine Learning

   Explainable AI

   Group Fairness

   Health Informatics

   SHIELD: A SHapley and Information-theory based framework for Equitable Learning via Dissimilar feature grouping

   Hyeonggeun Yun, Hanna Suominen^*, and Amanda Barnard^*

   The Australian National University, Oct 2025

   DOI PDF Artefact Poster

Preprints

2026

1. Machine Learning

   Neural Network

   Learning from Biological Networks: Which Organisational Principles Improve Graph Neural Networks?

   Ananya Rajesh, Hyeonggeun Yun, and Shahadat Uddin^*

   (Submitted to Expert Systems With Applications), Sep 2026

   Artefact
2. Machine Learning

   Individual Fairness

   IF-CRED: Validating the Evaluation of Individual Fairness

   Hyeonggeun Yun and Shahadat Uddin^*

   (Submitted to Engineering Applications of Artificial Intelligence), Sep 2026

   Artefact
3. Machine Learning

   Group Fairness

   Statistically Robust Evaluation and EMD-Driven Optimisation of Preprocessing for Fair Machine Learning

   Hyeonggeun Yun and Shahadat Uddin^*

   (Submitted to International Journal of Data Science and Analytics), Jun 2026

   Artefact
4. Machine Learning

   Explainable AI

   Group Fairness

   Health Informatics

   Equitable learning via dissimilar variable grouping for synthetic healthcare data

   Hyeonggeun Yun, Hanna Suominen^*, and Amanda Barnard^*

   (Submitted to Digital Health), Mar 2026

   Artefact