Inherent Interpretability
Inherent interpretability in machine learning focuses on designing models and methods that are inherently transparent and understandable, aiming to reduce the "black box" nature of many AI systems. Current research emphasizes developing intrinsically interpretable model architectures, such as those based on decision trees, rule-based systems, and specific neural network designs (e.g., Kolmogorov-Arnold Networks), alongside techniques like feature attribution and visualization methods to enhance understanding of model behavior. This pursuit is crucial for building trust in AI, particularly in high-stakes applications like healthcare and finance, where understanding model decisions is paramount for responsible deployment and effective human-AI collaboration.
Papers
xLP: Explainable Link Prediction for Master Data Management
Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala, Neeraj R Singh, Gayatri Mishra, Sumit Bhatia, Hima Patel, Somashekar Naganna, Sameep Mehta
Pantypes: Diverse Representatives for Self-Explainable Models
Rune Kjærsgaard, Ahcène Boubekki, Line Clemmensen
DSEG-LIME: Improving Image Explanation by Hierarchical Data-Driven Segmentation
Patrick Knab, Sascha Marton, Christian Bartelt
Fast and Simple Explainability for Point Cloud Networks
Meir Yossef Levi, Guy Gilboa
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques
Hao-Ting Pai, Yu-Hsuan Kang, Wen-Cheng Chung
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models
Hengyuan Zhang, Zitao Liu, Chenming Shang, Dawei Li, Yong Jiang
Multiple Instance Learning with random sampling for Whole Slide Image Classification
H. Keshvarikhojasteh, J. P. W. Pluim, M. Veta
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning
Chenglong Wang, Yinqiao Yi, Yida Wang, Chengxiu Zhang, Yun Liu, Kensaku Mori, Mei Yuan, Guang Yang