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              <p>This paper presents the outcomes of the DEEL (DEpendable and Explainable Learning) program-an innovative Franco-Québec collaboration focused on enabling the safe and certifiable use of AI in safety-critical systems. Drawing on the experience and insights of the DEEL project, this paper examines key questions concerning the advancement of AI systems in safety-critical domains, focusing on their operational capabilities and the challenges associated with certification. We explore major challenges such as explainability, robustness, statistical guarantees, out-of-distribution detection, and fairness, and critically assess both the advances made and the limitations that remain-offering guidance for the continued development and certification of safety-critical AI systems. The DEEL program has not only produced novel and advanced research methodologies but has also resulted in multiple open-source libraries and datasets that bridge the gap between theoretical AI research and practical real-world applications. The insights and tools developed through the DEEL program demonstrate the potential for creating safer, more reliable, and more understandable AI systems, paving the way for further research in this important field.</p>
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