Search Results for author:
Found 3 papers, 0 papers with code
Leveraging Explanations in Interactive Machine Learning: An Overview
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model.
User Driven Model Adjustment via Boolean Rule Explanations
AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic.
FROTE: Feedback Rule-Driven Oversampling for Editing Models
However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
