Explainable AI (XAI) Training in Pierre

This instructor-led, live training in Pierre (online or onsite) is aimed at intermediate-level professionals who wish to apply Explainable AI techniques to ensure fairness, transparency, and ethical AI systems for government.

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

• Understand the role of XAI in ethical AI systems within public sector workflows.
• Implement XAI techniques to detect and mitigate bias in AI models for government applications.
• Ensure transparency in AI model decision-making processes to enhance accountability for government operations.
• Align AI development with ethical and regulatory standards for government compliance.

This instructor-led, live training in Pierre (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems for government.

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

• Comprehend the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Analyze decisions made by deep learning models.
• Assess the trade-offs between performance and transparency in AI systems for government.

This instructor-led, live training in Pierre (online or onsite) is designed for advanced-level professionals who wish to deepen their understanding of Explainable Artificial Intelligence (XAI) techniques for complex AI models.

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

• Implement state-of-the-art XAI techniques in AI models.
• Interpret the decisions made by deep learning models.
• Apply advanced model-agnostic and model-specific explainability methods.
• Address challenges related to AI transparency in complex systems, ensuring alignment with public sector workflows for government.

This instructor-led, live training in Pierre (online or onsite) is designed for intermediate to advanced-level professionals who aim to implement transparent artificial intelligence systems in real-world applications for government.

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

• Comprehend the significance of transparency in AI models.
• Apply advanced explainable AI (XAI) techniques to interpret complex models.
• Improve model transparency using SHAP, LIME, and other tools for government applications.
• Tackle ethical concerns and ensure fairness in AI models for government use.

This instructor-led, live training in Pierre (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of Explainable AI and its role in developing responsible AI systems for government.

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

• Understand the fundamental concepts of Explainable AI.
• Explore the importance of transparency and interpretability in AI models within public sector workflows.
• Learn basic techniques for making AI models explainable, ensuring alignment with governance and accountability standards.
• Apply XAI techniques to simple machine learning models, enhancing their applicability for government use.