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As an official training partner, we offer courses on responsible AI to Positive AI members as well as to all companies involved in ethical AI topics. Our tailored programs enable employees, executives, and technical experts to acquire the essential skills to design, deploy, and oversee ethical and transparent AI systems.

Through this collaboration, we share a common goal: to promote innovative and responsible practices in the use of AI. Together.

See our training programs
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Our Training Programs:
The Result of This Collaboration

Formation Positive AI - Ethical Leader

Duration: 1h30
Format : in-person or online
  • Instructors: AI & Data Experts

Why Take This Training?

Artificial intelligence is transforming our businesses and societies. But how can we ensure that it is used ethically and responsibly? This training provides you with the tools to identify risks, comply with regulations, and build trust in your AI projects.

I'm Interested!

What You Will Learn

  • Understand the ethical challenges and their real-world impacts
  • Identify and mitigate biases in data and algorithms
  • Discover the latest regulations and legal obligations
  • Implement responsible practices at every stage of an AI projec

Training Program

Ethics and Responsibility in Data and AI (60 min)
  • Why ethics is essential (societal impact, regulations, reputation)
  • Ethical challenges: bias, discrimination, inclusivity
  • Real-world case studies: CV screening, facial recognition, AI & disability
  • Examples of ethical “fails” in AI and their consequences
Building Ethical and Responsible AI (40 min)
  • Awareness and engagement at all levels
  • Integrating ethical principles from the design phase
  • The key role of model explainability
  • Overview of current and upcoming regulations (GDPR, AI Act, UNESCO…)
I want to train!

Who is this training for?

  • Managers & decision-makers involved in AI projects
  • Data Scientists & AI Engineers looking to design responsible models
  • Legal professionals & Compliance Officers concerned with AI regulations

Positive AI Training – Master Ethics and Fairness in AI

Duration: 4 days (28 hours)
In-person or online
  • Audience: Data Scientists, AI Engineers, AI Managers & Decision-Makers, Compliance Experts
  • Instructors: AI, Data & Ethics Experts

Why Take This Training?

AI is transforming our society, but it raises many ethical challenges. This comprehensive training enables you to:

  • Detect and mitigate biases in data and algorithms
  • Master current regulations (GDPR, AI Act…)
  • Deploy compliant and transparent AI models
  • Apply responsible strategies for a positive impact
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Key Strengths of the Training

1

Balance Between Theory and Practice

Real-world case studies and hands-on simulations

 

2

Advanced Tools

Bias detection, model explainability, and mitigation techniques

3

Regulatory Focus

Compliance, auditability, and accountability in AI

4

Environmental Impact of Data Science

Best practices for sustainable AI

5

Collaborative Projects

Apply concepts to real-world use cases

Group Projects: Apply Your Knowledge to Real-World Cases

Throughout the training, you will work on a collaborative project focused on AI bias and ethics. In teams, you will analyze a biased dataset, identify issues, and propose solutions using advanced correction and explainability techniques.

  • Project selection from various predefined challenges (e.g., bias in CV screening, facial recognition, spam detection)
  • Data exploration and bias identification
  • Implementation of mitigation techniques (pre-processing, post-processing, explainability)
  • Deployment of a compliant, auditable model
  • Final presentation of results
  • Guidance from a DataScientest mentor throughout the project
Day 1: Bias Detection (6h)
  • Introduction to bias in Machine Learning: definition, real-world examples, and impact
  • Hands-on exercise with a biased dataset to highlight disparities and their effects on model predictions
Day 2: Mitigating Discriminatory Bias in Algorithms (7h)
  • Applying fairness principles in Machine Learning
  • Implementing bias reduction strategies during the modeling process
  • Practical workshop on mitigation methods (pre-processing, learning, post-processing)
  • Exploring fairness metrics and open-source libraries
  • Case studies covering various sources of bias in data
Day 3: Model Interpretation, Ethics, and Deployment (7h)
  • The importance of model interpretability from ethical and legal perspectives
  • Hands-on exercises with interpretability tools such as Shapley values and sensitivity analyzers
  • Case study: Interpreting a biased spam detection algorithm in Python
  • Best practices for model traceability, monitoring, deployment, and documentation
Day 4: Data Science and Environmental Impact (2h)
  • Understanding the environmental impact of digital technologies
  • Exploring actionable solutions to reduce AI’s carbon footprint
  • Practical exercise on implementing sustainable data science strategies

Formation Positive AI – Fundamentals

Duration: 3 hours
In-person or online
  • Audience: Data Scientists, AI Engineers, AI Managers & Decision-Makers
  • Instructors: AI, Data & Ethics Experts

Why Take This Training?

This training will help you:

  • Understand the key concepts of ethics and responsibility in AI
  • Identify ethical challenges in data and algorithm management
  • Apply responsible principles at every stage of the AI project lifecycle
  • Prepare for legal requirements and future initiatives (AI Act, GDPR…)
I'm Interested!

Key Strengths of the Training

1

Clear Introduction to Ethics and Responsibility in the Context of AI

2

Real-World Case Studies: Bias in data, algorithmic discrimination, and ethical failures in AI

3

Interactive Discussion: Sharing experiences and best practices with participants

4

Focus on Current Legislation and Future Perspectives (AI Act, GDPR, etc.)

5

Practical Advice: How to implement ethical and responsible AI

Contenu de formation

I. Introduction (20 min)
  • Training objectives
  • Presentation of the instructor and program structure
II. Ethics and Responsibility (20 min)
  • Understanding ethics in general: key principles and differences with responsibility
  • Definition and comparison between ethics and responsibility
III. Ethics and Responsibility in Data and AI (80 min)
  • Why ethics and responsibility are crucial in AI: societal, legislative, and reputational impact
  • Bias in data and algorithms: mechanisms, sources, and real-world examples (e.g., CV bias, facial recognition, AI for disability)
  • Case studies of ethical FAILs in AI: decision-making systems, image cropping on Twitter, Word2Vec
  • Live demo of GenAI with visible biases
IV. Building Ethical and Responsible AI (40 min)
  • Raising awareness and the role of each stakeholder in the process
  • Developing ethical and responsible AI: principles to follow from the design phase
  • Legislative aspects: GDPR, CCPA, AI Act, and other legal frameworks
V. Conclusion and Closing (20 min)
  • Recap of key takeaways from the training
  • Resources to deepen the topic: readings, conferences, etc.
  • Participants’ commitment to applying the principles learned in their AI projects
I want to train!

The Strengths of Our Training

🚀 Led by renowned experts in AI & Data
🛠️ Pragmatic approach, illustrated with real-world case studies
📊 Live demo of AI biases and their impacts
📜 A training up-to-date with the latest legislative developments

Learn More About the Partnership

The partnership between DataScientest and Positive AI aims to promote responsible and ethical artificial intelligence. By combining technical expertise with a commitment to sustainable digital practices, we support the training of tomorrow’s talent while fostering practices aligned with strong ethical values.

Discover how this collaboration is shaping the AI of tomorrow.

I'm Interested!
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