Explainable AI Methods for Transportation & Logistics Industries

This course is designed to provide a comprehensive understanding of Explainable AI (XAI) methods and their applications in the Transportation & Logistics industries. Participants will learn about the importance of transparency, interpretability, and accountability in AI systems and explore various techniques for developing explainable AI models. The course covers both model-agnostic and model-specific XAI methods, as well as strategies for communicating AI insights to stakeholders. Through hands-on exercises and real-world case studies, participants will gain practical skills in implementing XAI techniques to build trust and improve decision-making in transportation and logistics AI applications.

• Understand the importance and challenges of explainability in AI systems for the Transportation & Logistics industries

• Apply model-agnostic XAI methods, such as SHAP, LIME, and Permutation Feature Importance

• Implement model-specific XAI techniques for deep learning models, such as Saliency Maps and Layer-wise Relevance Propagation

• Develop visualizations and user interfaces for communicating AI insights to technical and non-technical stakeholders

• Evaluate and compare different XAI methods for specific transportation and logistics AI applications

1. Introduction to Explainable AI

• Overview of Explainable AI (XAI) and its importance in the Transportation & Logistics industries

• Challenges and limitations of black-box AI models

• Taxonomy of XAI methods (e.g., model-agnostic, model-specific, local, global)

• Ethical and regulatory considerations for explainable AI

• Hands-on exercises: Exploring the impact of explainability on AI decision-making in transportation and logistics scenarios

2. Model-Agnostic XAI Methods

• Feature importance techniques (e.g., Permutation Feature Importance, Partial Dependence Plots)

• Local Interpretable Model-agnostic Explanations (LIME)

• Shapley Additive Explanations (SHAP) and its variants (e.g., KernelSHAP, TreeSHAP)

• Counterfactual explanations and their applications in transportation and logistics AI systems

• Hands-on exercises: Implementing model-agnostic XAI methods on transportation and logistics datasets

3. Model-Specific XAI Methods

• Saliency Maps and Gradient-based methods for deep learning models

• Layer-wise Relevance Propagation (LRP) and DeepLIFT

• Concept Activation Vectors (CAVs) and their applications in interpretable AI

• Attention mechanisms and their role in explainable AI for sequential data

• Hands-on exercises: Applying model-specific XAI techniques to deep learning models for transportation and logistics applications

4. Visualizations and User Interfaces for XAI

• Principles of effective data visualization for explainable AI

• Interactive visualizations and dashboards for exploring AI insights

• Designing user interfaces for explainable AI systems

• Strategies for communicating AI explanations to technical and non-technical stakeholders

• Hands-on exercises: Developing visualizations and user interfaces for XAI in transportation and logistics AI applications

5. Evaluation and Best Practices for XAI

• Evaluation metrics and frameworks for assessing the quality of AI explanations

• Comparative analysis of different XAI methods for specific transportation and logistics AI tasks

• Best practices for implementing and deploying explainable AI systems in the Healthcare & Life Sciences industries

• Case studies of successful XAI applications in transportation and logistics domains

• Hands-on exercises: Evaluating and comparing XAI methods for a real-world transportation and logistics AI application

• Strong understanding of machine learning concepts and algorithms

• Proficiency in programming with Python and familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

• Knowledge of data visualization techniques and libraries (e.g., Matplotlib, Seaborn) is beneficial but not required