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Found 12 papers, 3 papers with code
Feature Importance versus Feature Influence and What It Signifies for Explainable AI
The Contextual Importance and Utility (CIU) method provides a unified definition of global and local feature importance that is applicable also for post-hoc explanations, where the value utility concept provides instance-level assessment of how favorable or not a feature value is for the outcome.
Context, Utility and Influence of an Explanation
AI developers can create ethical systems that benefit society by considering contextual factors like societal norms and values.
Do intermediate feature coalitions aid explainability of black-box models?
This work introduces the notion of intermediate concepts based on levels structure to aid explainability for black-box models.
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.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
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 artificial intelligence
Explainable Artificial Intelligence (XAI)
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.
Explainable Artificial Intelligence (XAI)
Feature Importance
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).
