Artificial Intelligence is increasingly used in clinical diagnostics; yet many systems fail to capture physician trust because their decision-making processes are unclear and difficult to interpret. To address this challenge, I propose the design of an explain…
Machine learning systems increasingly make decisions with long-term consequences for individuals and society. Designing trustworthy machine learning systems therefore requires going beyond static accuracy and one-shot fairness criteria. Decisions may alter fut…
Recent advances in machine learning and deep learning have achieved remarkable success across a wide range of applications, yet they largely rely on data-driven paradigms that assume the training and test data share similar distributions. In real-world scenari…
Intelligence artificielle digne de confiance grâce à l'apprentissage fédéré équitable et respectueux de la vie privée L'augmentation massive du volume de données disponibles a transformé l'informatique moderne, contribuant à l'essor de l'apprentissage automati…
Vertical Federated Learning (VFL) enables organizations with distinct features of the same data samples to collaboratively train machine learning models without directly sharing raw data. While VFL offers strong potential for privacy-preserving analytics in do…
Apprentissage automatique de confiance pour les systèmes de détection d'intrusion Les systèmes de détection d'intrusion sont des composants essentiels à la défense de notre écosystème numérique. Récemment, les avancées en apprentissage automatique ont permis d…
The increasing adoption of machine learning (ML) and artificial intelligence (AI) in critical decision-making has intensified demands for explainability and transparency, particularly in high-risk domains such as finance. This dissertation investigates how exp…
Recent years have seen instability in the electricity market and increasing energy demands in the world. Reliable electricity consumption forecasting is a helpful tool for consumers and other stakeholders to manage energy consumption more efficiently. This the…
Artificial intelligence has become a key driver of innovation in geospatial data analysis. It enables new prediction, classification and decision-making methods in areas such as urban planning, environmental monitoring and mobility services. However, many arti…
Artificial intelligence (AI) has become deeply embedded in modern decision-making systems, driving advances across science, industry and society. Yet, as models grow in scale and complexity, they increasingly operate as opaque systems whose internal reasoning…
Large language models (LLMs) are increasingly serving as a primary interface for accessing information and interacting with digital systems. Nonetheless, LLMs can generate text that includes incorrect or unsupported claims while being fluent, coherent, and pre…
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قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
