AI Transparency and Explainability Training

Unlock Trust and Compliance in Your AI Solutions
Why This Training?
In the evolving landscape of artificial intelligence, transparency and explainability are paramount. As the EU AI Act emphasizes clear and understandable AI models, businesses must align to foster trust and meet regulatory standards. This training offers a deep dive into constructing AI solutions that are both transparent and explainable, ensuring that your AI team remains ahead of the curve and compliant.
Duration: 1 Day (8 hours), (online / virtual live session)

Who Should Attend?

 AI Developers and Engineers
 Data Scientists and Analysts
 Compliance Officers and Legal Teams
 Product Managers overseeing AI solutions
 CTOs, CIOs, and Tech Leadership
 Anyone interested in understanding the intricacies of transparent AI

Course Highlights

 Introduction to AI Transparency and Explainability

  • Importance in today's AI landscape
  • Overview of the EU AI Act mandates

 Technical Dive into Transparent AI Models
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  • AI model architecture for transparency
  • Features and decision-making processes

 Interpreting AI with SHAP and LIME
  • Techniques for model interpretation
  • Practical applications and examples

 Challenges and Ethical Implications
  • Trade-offs between model performance and transparency
  • Ethical considerations for transparent AI

 Hands-on Workshops
  • Build and evaluate transparent AI models
  • Real-world case studies and analysis


 Basic understanding of AI and machine learning concepts
 Familiarity with AI development processes

Training Materials Needed by Participants

Laptop with Python environment set up
AI development tools (suggested list will be provided prior to training)
Pre-training reading materials (to be provided upon registration)
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Training Content

AI Transparency and Explainability Training

Objective: Equip your AI team with the knowledge and tools needed to construct transparent and explainable AI models. Dive deep into the methodologies, ensuring alignment with the EU AI Act and fostering trust and compliance in your AI solutions.

Session 1: Introduction to AI Transparency and Explainability

  • Demystifying Transparent AI: Understanding the need and importance.
  • The EU AI Act: An overview of mandates on transparency and explainability.
  • Real-world Implications: Case studies where AI transparency made a difference.

Session 2: The Technical Landscape of Transparent AI

  • AI Model Architecture: Key features that enable transparency.
  • Decisions in AI: Unpacking how AI models make decisions.
  • Interactive Activity: A walkthrough of a transparent AI model.

Session 3: Methods for Achieving Explainability

  • Model Interpretation with SHAP and LIME: Techniques overview.
  • Trade-offs: Performance vs. transparency in AI models.
  • Hands-on Workshop: Crafting models with SHAP and LIME.

Session 4: Challenges and Ethical Implications in Transparent AI

  • Navigating Complexities: Achieving balance in transparency.
  • Ethical Implications: What happens when AI models aren't transparent?
  • Group Discussion: Ethical challenges faced by attendees in their domains.

Session 5: Real-world Application and Case Studies

  • Industry-wise Breakdown: How different sectors achieve AI transparency.
  • Case Study Analysis: Dive deep into specific instances of transparent AI.
  • Interactive Workshop: Analyzing and discussing real-world AI models.

Session 6: Regulatory and Compliance Considerations

  • Understanding the EU AI Act: A deeper dive.
  • Achieving Compliance: Steps and measures to ensure alignment.
  • Group Activity: Designing a compliance checklist for AI models.

Session 7: Hands-on Workshop: Crafting Transparent AI Solutions

  • Activity Brief: Working on a given AI scenario.
  • Group Tasks: Constructing transparent AI models.
  • Feedback Rounds: Sharing and refining the models developed.

Session 8: Concluding Thoughts and Engaging Q&A

  • Recap of the Training: Highlighting key takeaways.
  • Open Floor Q&A: Addressing any lingering questions.
  • Path Forward: Encouraging implementation of learnings and best practices in attendees' projects.
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