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Found 6 papers, 1 papers with code
EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
Explanations in interactive machine-learning systems facilitate debugging and improving prediction models.
Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform
Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations.
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing.
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
Results indicate that our participants preferred our representation of data-centric explanations that provide local explanations with a global overview over other methods.
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models
To validate AHMoSe, we describe a use case scenario in the viticulture domain, grape quality prediction, where the system enables users to diagnose and select prediction models that perform better.
Interpretability of machine learning based prediction models in healthcare
There is a need of ensuring machine learning models that are interpretable.
