Explainability for Machine Learning Models: From Data Adaptability to User Perception

arXiv:2402.10888v1 Announce Type: cross
Abstract: This thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations considering both data and user requirements. The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful to the underlying model and comprehensible to the users.
The thesis is divided into two parts. The first enhances a widely used rule-based explanation method. It …

abstract adaptability arxiv cs.ai cs.hc cs.lg data explainability identify machine machine learning machine learning models perception requirements thesis type