IA Responsable : Transparence et Éthique

Aperçu

Did you know that while 75% of business leaders agree AI ethics is important, most admit they lack the necessary tools or frameworks to implement it? According to Datamation, the majority of companies recognize the significance of AI ethics but struggle with practical implementation.

The gap between knowing and doing is massive—and that’s where this course comes in. Responsible AI isn’t just about feeling ethical.

It’s about building systems that are safer, smarter, and more transparent from the ground up This course is designed for professionals who are shaping the future of artificial intelligence. It’s ideal for data scientists, machine learning engineers, AI project managers, product leads, compliance officers, policy advisors, and ethics reviewers.

Whether you're developing AI systems or ensuring they meet ethical and regulatory standards, this course equips you with the tools and knowledge to build responsible, unbiased AI applications. To get the most from this course, learners should have a basic understanding of machine learning workflows and the AI lifecycle.

Familiarity with general technology concepts and the ability to prompt tools like ChatGPT will be helpful. While prior experience with Python or Jupyter Notebooks is beneficial, it’s not mandatory—this course is built to be accessible and practical.

By the end of the course, learners will be able to identify and mitigate bias in AI systems, implement explainability tools like SHAP and LIME, and develop responsible AI checklists based on fairness and transparency. They will also learn to evaluate AI projects against compliance frameworks such as the NIST AI Risk Management Framework, ensuring that their systems are ethical, explainable, and aligned with industry standards.

Programme

• IA Responsable : Transparence & Éthique
Dans ce cours, vous explorerez des outils pratiques et des cadres pour construire des systèmes d'IA éthiques et fiables. Grâce à une expérience pratique avec des outils comme AI Fairness 360, SHAP et LIME, vous apprendrez à auditer les modèles pour détecter les biais, améliorer l'explicabilité et intégrer la transparence et la conformité dans votre flux de travail. Vous développerez et appliquerez également des listes de contrôle pour une IA responsable, garantissant que vos projets d'IA s'alignent avec les normes éthiques et réglementaires sans compromettre l'innovation.

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Enseigné par

Brian Newman and Starweaver Instructor Team

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Matières

Computer Science