Ethical Issues of AI
Explore the crucial ethical issues of Artificial Intelligence and learn to integrate a responsible approach into your AI projects. This in-depth training will allow you to understand the fundamental principles of AI and their implications for individual and institutional ethics. You will develop a critical vision of AI by becoming aware of its limits, while acquiring the necessary skills to identify and mitigate algorithmic biases and transparency problems. You will also explore the environmental challenges related to AI and strategies to minimize its ecological impact. Through practical workshops and case studies, you will learn to integrate ethics into your strategic thinking around AI and to lead ethical AI projects, in compliance with current regulations.
What you will learn
- Understand the fundamental principles of AI and their implications for individual and institutional ethics.
- Develop a critical vision of AI by becoming aware of its limits.
- Acquire the necessary skills to identify and mitigate algorithmic biases and transparency problems.
- Explore the environmental challenges related to AI and strategies to minimize its ecological impact.
- Integrate ethics into strategic reflections around AI.
- Lead ethical AI projects, compliant with current regulations.
- Become a key player in responsible AI.
Prerequisites
- Have completed the "Introduction to the fundamental principles of AI" training or have initial knowledge of artificial intelligence technologies.
Target audience
- Professionals who already have a basic knowledge of AI and wish to deepen their skills in managing the ethical issues related to this technology.
Training Program
3 modules to master the fundamentals
Topics covered
- →Impact study and analysis
- →Taking responsibility
- →Supervision by regulatory authority
- →Ensuring continuous improvement
- →Training and awareness
- →Production of documentation
- →Continuous evaluation
Objectives
- Strengthen the understanding of AI by bringing out concrete examples of application in the candidates' own professional fields.
Topics covered
- →Reflection on professional irritants to be presented to the group.
- →Proposal of a solution based on AI or generative AI for each identified irritant.
Objectives
- Raise participants' awareness of biases in data and lead them to develop skills to identify them.
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote