Explainable AI Methods

This course dives into the world of Explainable AI (XAI) methods, equipping you to understand how AI models in Supply Chain Management (SCM) arrive at their decisions. You'll explore various XAI techniques that unlock the "black box" of AI models, fostering trust, transparency, and responsible application of AI for optimizing your supply chains.

• Define the importance of Explainable AI for building trust and transparency in AI-powered SCM solutions.

• Explore different categories of XAI methods (model-agnostic vs. model-specific techniques).

• Understand how to interpret the outputs of various XAI techniques for gaining insights into model behavior.

• Apply XAI techniques to analyze and explain the decision-making processes of AI models used in specific SCM tasks (e.g., demand forecasting).

• Identify the limitations of XAI methods and best practices for responsible XAI implementation in SCM.

1. Unveiling the Black Box of AI

• Introduction to Explainable AI (XAI): Understanding the need for XAI in building trust and responsible AI use within SCM.

• Demystifying the "Black Box" Problem: Exploring the challenges of interpreting complex AI model decisions and their impact on SCM processes.

• Exploring Different Categories of XAI Techniques: Model-agnostic methods (e.g., LIME) vs. model-specific techniques (e.g., feature importance for decision trees).

• Hands-on Exercises (Optional): Utilizing online tools or libraries to explore basic XAI techniques on pre-trained models for SCM tasks (e.g., interpreting a demand forecasting model).

• Case Studies: Examining real-world applications of XAI in SCM, such as explaining AI-driven recommendations for inventory optimization.

2. XAI for Transparency and Responsible AI

• Deep dive into Specific XAI Techniques: Understanding how methods like LIME or feature attribution work to explain model predictions.

• Applying XAI to Real-World SCM Applications: Utilizing XAI techniques to analyze and explain AI models used in specific SCM tasks chosen by you (e.g., understanding how an AI model prioritizes deliveries).

• Hands-on Exercises (Optional): Working with XAI tools or libraries to analyze an AI model relevant to your SCM domain (may involve basic coding).

• Limitations of XAI Methods and Responsible Implementation: Understanding the boundaries of XAI and best practices for ethical AI use in SCM.

• Course Wrap-up: Exploring the future of XAI and its potential for fostering human-AI collaboration in supply chain decision-making.

• 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