Ethics, Bias and Responsibility in the Use of AI

🔸 Context and Challenges

Generative AI is rapidly integrating into companies, but its use involves often underestimated risks:

• Factual errors

• Discriminatory biases

• Breaches of confidentiality

• Excessive dependence on the tool

• Legal risks (GDPR, intellectual property)
• Erroneous decisions impacting the company or employees

Without awareness, these risks can lead to operational, human, and legal consequences.

🔸 Training Benefits

This training program enables participants to:

• Understand how and why AI can be wrong

• Identify biases and ethical risks in generated responses

• Adopt a responsible posture to protect the company and themselves

• Verify, cross check, and secure daily AI usage

• Recognize situations where AI should not be used

🔸 Key Strengths of the Training

1. Concrete examples of risks encountered in companies

2. Pedagogical approach focused on safety, responsibility, and error prevention

3. Immediate hands on practice to learn how to detect biases

4. Training delivered by an expert in AI and digital ethics

🔸 Objectives

1. Evolution Objectives

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

• Identify risks related to AI usage

• Detect biases in AI generated responses

• Adopt an ethical and responsible approach

• Protect data, the company, and their own responsibility
• Apply best practices in their professional context

2. Learning Objectives

Participants will acquire:

• Basics of how AI works and its limitations

• Clear understanding of cognitive and algorithmic biases

• Essential rules of security, confidentiality, and compliance

• Methods to analyze, verify, and correct AI responses
• Concrete examples of risks and best practices

🔸 Example of Practical Application

Context: An employee uses AI to write a client message, analyze a CV, or produce an internal report. The generated response contains biases, errors, or poorly managed confidential information

Objective: Understand associated risks, identify biases, correct the response, and adopt a responsible approach.

Work Completed:

• Analysis of an AI generated response

• Detection of biases (stereotypes, discrimination, factual errors)

• Identification of risks (confidentiality, GDPR, reputation)
• Reformulation of the prompt to reduce risks
• Implementation of a human verification protocol

Skills Mobilized:

• Critical analysis

• Bias detection

• Verification and cross checking
• Reformulation
• Application of ethical and legal best practices