Course Outline
Introduction to Deep Learning Explainability for Government
- What are black-box models?
- The importance of transparency in AI systems for government operations
- Overview of explainability challenges in neural networks for government applications
Advanced XAI Techniques for Deep Learning for Government
- Model-agnostic methods for deep learning: LIME, SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based methods
Explaining Neural Network Decisions for Government
- Visualizing hidden layers in neural networks to enhance transparency for government use cases
- Understanding attention mechanisms in deep learning models for improved decision-making in public sector applications
- Generating human-readable explanations from neural networks to support accountability and trust in government systems
Tools for Explaining Deep Learning Models for Government
- Introduction to open-source XAI libraries suitable for government projects
- Using Captum and InterpretML for deep learning in government contexts
- Integrating explainability techniques in TensorFlow and PyTorch for government applications
Interpretability vs. Performance for Government
- Trade-offs between accuracy and interpretability in the context of government AI systems
- Designing interpretable yet performant deep learning models for government use
- Handling bias and fairness in deep learning to ensure equitable outcomes in government services
Real-World Applications of Deep Learning Explainability for Government
- Explainability in healthcare AI models to support public health initiatives
- Regulatory requirements for transparency in AI for government compliance
- Deploying interpretable deep learning models in production environments for government agencies
Ethical Considerations in Explainable Deep Learning for Government
- Ethical implications of AI transparency in government operations
- Balancing ethical AI practices with innovation in the public sector
- Privacy concerns in deep learning explainability within government contexts
Summary and Next Steps for Government
Requirements
- Advanced knowledge of deep learning techniques
- Familiarity with Python programming and deep learning frameworks
- Experience in developing and working with neural networks
Audience
- Deep learning engineers for government
- AI specialists
Testimonials (5)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Very flexible.
Frank Ueltzhoffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
