#explainable AI
35 papers match
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
Muhammad Adil Saleem, Syed Ali Raza, Mary-Anne Williams
The paper investigates adding the predicted class of an instance to the reinforcement‑learning state representation for generating counterfactual explanations, showing that this cl…
Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
Xu Zheng, Wei Cheng, Zhuomin Chen +3
The paper presents IB-Forecast, an interpretable multivariate time-series forecasting model that uses an information bottleneck to generate sparse, faithful explanations of predict…
Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations
Roel Visser, Isaac Roberts, Barbara Hammer
The paper proposes Contrastive Concept Importance (CCI), a method that attributes the logit margin between a target and a foil class to automatically extracted visual concepts, pro…
Automated Multilabel Mpox Research Classification with Explainable Transformer Models
Tanjim Taharat Aurpa
The paper develops a BERT‑based multilabel classifier to automatically assign 14,590 Mpox research articles to topics such as outbreaks, vaccination, and epidemiology, and uses SHA…
A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor
Yin Lin, Elena De Martin, Giacomo Conte +6
The paper introduces a web‑based visual analytics platform that integrates cohort management, radiomic feature extraction, and guarded inference with pre‑trained machine learning m…
INCLAIR: Inception-Based Longitudinal Clinical Anomaly Detection with Informed Reasoning
Maxx Richard Rahman, Wolfgang Maass
INCLAIR is a framework that detects anomalies in longitudinal clinical records by scoring observations against multiple historical contexts, aggregating evidence, and producing nat…