Trusted AI Advanced Master’s Degree

Next sessions:

To be determined

Overview

As Artificial Intelligence rapidly expands, mastering Trusted AI technologies has become a strategic priority for industrial competitiveness, economic performance, and national sovereignty. This Trusted AI Advanced Master’s Program, designed through a strategic partnership between CentraleSupélec Exed and IRT SystemX, aims to train experts capable of designing, deploying, and auditing AI systems that meet high standards of reliability, transparency, and ethics.

Co-developed with industry players and based on the needs identified in Confiance.ai program projects, this pioneering training combines academic excellence with operational expertise. It is tailored for professionals and recent graduates who want to contribute concretely to the responsible integration of AI into the complex systems of tomorrow.

Objectives

By the end of the program, participants will be able to:

• Design new products integrating AI components
• Identify relevant use cases where AI provides real value
• Adapt existing solutions with more flexible, robust, and ethical AI technologies
• Independently acquire new AI skills through scientific and technological watch

Program

1. Data Science and Learning Techniques for Trustworthy AI

• Machine Learning, Deep Learning
• Data and knowledge management: acquisition, storage, preparation
• Building and qualifying training datasets

2. Introduction to Systems Engineering for Trusted AI

• Global approach for AI components with controlled trust
• Legal and ethical challenges

3. Trust Evaluation

• Relevance of evaluation methods, robustness, explainability (XAI)
• Bias detection, KPI and metric definition
• Practical tool implementation

4. Systems Engineering with AI Components

• Defining the Operational Design Domain (ODD)
• IVVQ strategy (Integration, Verification, Validation, Qualification)
• Standards and norms in data science and AI
• Embedded AI and industrial constraints

5. Trust and Human Interaction

• Human-AI system interaction
• Designer/certifier interaction for AI systems

6. Case Studies & Capstone Project

• Industrial case studies (3-hour blocks)
• Capstone project: 4 dedicated days for kick-off and project follow-up
• Professional thesis: real-world application in a work context

Teaching methods

The training uses a hybrid pedagogy, combining theoretical courses, case studies, hands-on sessions, and real-world immersion through a capstone project and a professional thesis. Participants benefit from cutting-edge technological resources at IRT SystemX, including experiment platforms and real industrial use cases, ensuring hands-on, practical learning.

Target Audience & Prerequisites

Target audience:

• Recent graduates (engineering, university, business school)
• Experienced professionals in software, hardware, or tech engineering
• Anyone aiming to design or integrate innovative AI solutions into products, services, or startups

Prerequisites:

Applicants must hold one of the following:

• A Master’s degree or RNCP Level 7 diploma (engineering, business, or university background)
• A Bachelor’s degree + 3 years of relevant professional experience
• An equivalent international degree

Key Strengths of the Training

• Co-developed with IRT SystemX, a key player in Trusted AI research
• Access to cutting-edge experimental platforms
• Real use cases from major programs focused on digital transformation and decarbonization
• A multidisciplinary program aligned with real-world industrial needs

Testimonies

https://www.youtube.com/watch?v=kjmFdYT_Fwc

Contact

academy@irt-systemx.fr

Read more

• Overview of the training