Sebastian Palacio

"Towards Interpretable Machine Learning Models for Computer Vision Problems"

The rising demand for machine learning (ML) models has become a key concern for stakeholders in diverse scenarios, as black-box solutions are continuously being implemented and relied upon. Consequently, an emergent field of ML has focused on intuitive notions of Explainable Artificial Intelligence (XAI), in an effort to fulfill requirements mostly related to safety and legal applications. In this work, current limitations in the field of XAI are being addressed, starting with the establishment of a framework that contextualizes, among others, the notions of “explainability” and “interpretability” for AI. Next, this thesis proposes a new method to generate visual explanations for state-of-the-art image classifiers, such that global patterns existing between the whole dataset and the model (deep convolutional neural networks) can be quantified. Finally, new model architectures are proposed that are “explainable by design”. These models explicitly convey low-level priors, providing richer, more structured predictions while maintaining or outperforming their black-box counterparts.

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