Category: François Pelletier, M.Sc.

François Pelletier, M.Sc.
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  • Aula Convening Guideline 2025 Ed.

    Aula Convening Guideline 2025 Ed.

    The Aula Convening Guidelines, 2025 ed.

    These Aula Convening Guidelines are for people working on tech governance and AI in society, these are 6 guidelines for convening communities for legitimate collective decision-making on how AI is implemented in society.

    Since our founding in 2023, Aula Fellows have hosted and participated in 100s of conversations in more than 30 countries and regions on AI. We have spoken with people who have a variety of needs, spanning through Learning AI, Living with AI, Working with AI, and Shaping AI.

    We have worked through 3 project phases, to develop these guidelines, from the common elements that make for conversations in which communities make decisions about AI. Our goal is not a new type of consultation, but rather to see to it that community convenings are conductive to collective decision making on AI.

    In 2026 we will be reaching out to partner organizations to continue to refine these guidelines and to bring them to more groups of people.

    They are complete and available now under a Creative Commons license, in this V.01, 2025 Edition.

    Link to the PDF.

  • Oui, mais je LLM !

    Oui, mais je LLM !

    L’IA générative nous joue des tours, en manipulant notre perception de la vérité en tentant de devenir notre confident et en créant une relation de dépendance. Mais, on peut aussi à notre tour l’utiliser pour extraire des informations privilégiées mal sécurisées, en utilisant des tactiques adaptées de l’ingénierie sociale.

    Le manque d’expérience autour de cette technologie et l’empressement à en mettre partout expose à de nouveaux risques.

    Je te présente un survol des concepts de base en cybersécurité revisités pour l’IA générative, différents risques que posent ces algorithmes et différents conseils de prévention pour bien les intégrer dans nos systèmes informatiques et notre pratique professionnelle.

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  • PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging

    PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging

    This paper describes PARADIM, a digital infrastructure designed to support research at the interface of data science and medical imaging, with a focus on Research Data Management best practices. The platform is built from open-source components and rooted in the FAIR principles through strict compliance with the DICOM standard. It addresses key needs in data curation, governance, privacy, and scalable resource management. Supporting every stage of the data science discovery cycle, the platform offers robust functionalities for user identity and access management, data de-identification, storage, annotation, as well as model training and evaluation. Rich metadata are generated all along the research lifecycle to ensure the traceability and reproducibility of results. PARADIM hosts several medical image collections and allows the automation of large-scale, computationally intensive pipelines (e.g., automatic segmentation, dose calculations, AI model evaluation). The platform fills a gap at the interface of data science and medical imaging, where digital infrastructures are key in the development, evaluation, and deployment of innovative solutions in the real world.

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