Lynda Dib is a researcher specializing in explainable artificial intelligence and model interpretability. She is currently conducting advanced research at Université Laval, focusing on adapting local interpretability methods, such as LIME, to enhance the transparency and usability of predictive models, with particular attention to refining generated explanations. Through an interdisciplinary approach, she bridges machine learning techniques with practical applications in healthcare, education, and other domains. Her work aims to contribute to the ethical and equitable use of AI by developing interpretable tools that support personalized interventions, model accountability, and the empowerment of practitioners and end users.