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Found 9 papers, 3 papers with code
Contextual Importance and Utility: aTheoretical Foundation
This paper provides new theory to support to the eXplainable AI (XAI) method Contextual Importance and Utility (CIU).
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts
For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data.
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain
We conducted three user studies based on the explanations provided by LIME, SHAP and CIU.
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series
Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process.
A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study
On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.
Cognitive Perspectives on Context-based Decisions and Explanations
When human cognition is modeled in Philosophy and Cognitive Science, there is a pervasive idea that humans employ mental representations in order to navigate the world and make predictions about outcomes of future actions.
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory
In this work, we report the practical and theoretical aspects of Explainable AI (XAI) identified in some fundamental literature.
Explainable AI without Interpretable Model
Especially if the AI system has been trained using Machine Learning, it tends to contain too many parameters for them to be analysed and understood, which has caused them to be called `black-box' systems.
Explanations of Black-Box Model Predictions by Contextual Importance and Utility
We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i. e. the causes of an individual prediction) and contrastive explanation (i. e. contrasting instance against the instance of interest).
