Explainability for Machine Learning Models: From Data Adaptability to
  User Perception
julien Delaunay
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 then introduces a novel approach for
evaluating the suitability of linear explanations to approximate a model.
Additionally, it conducts a comparative experiment between two families of
counterfactual explanation methods to analyze the advantages of one over the
other. The second part focuses on user experiments to assess the impact of
three explanation methods and two distinct representations. These experiments
measure how users perceive their interaction with the model in terms of
understanding and trust, depending on the explanations and representations.
This research contributes to a better explanation generation, with potential
implications for enhancing the transparency, trustworthiness, and usability of
deployed AI systems.