Explainable AI Methods for Customer Experience (CX)
Course Overview:
This course equips Customer Experience (CX) and Customer Service Management (CSM) professionals with a practical understanding of Explainable AI (XAI) methods. You'll explore techniques for understanding how AI models make decisions, leading to more transparent and trustworthy AI applications for enhancing customer experiences.
Learning Objectives:
Explain the importance of Explainable AI (XAI) in building trust and transparency in AI-powered CX applications.
Identify different XAI techniques for understanding how AI models arrive at their predictions.
Apply model-agnostic XAI methods to interpret the inner workings of pre-trained black-box models commonly used in CX tasks.
Utilize feature importance analysis and counterfactual explanations to gain insights into model decision-making for specific customer scenarios.
Communicate the results of XAI analysis to stakeholders in a clear and concise manner.
Course Highlights:
1. Why Explainable AI Matters for CX:
Introduction to Explainable AI (XAI): Understanding the concept of XAI and its critical role in building trust and transparency in AI models used for customer experience tasks.
The Black Box Problem: Exploring the limitations of complex AI models (e.g., deep neural networks) and the challenges in understanding their decision-making process.
Case Study 1: Analyzing the impact of a lack of explainability in a customer churn prediction model, highlighting the importance of understanding why customers leave.
Interactive Workshop: Working with a simple black-box model to experience the challenges of interpreting its predictions without explanation methods.
Introduction to Model-Agnostic XAI Techniques: Understanding the concept of model-agnostic XAI methods that can be applied to any pre-trained model, regardless of its internal architecture.
2. Unveiling Model Decisions with XAI Techniques:
Feature Importance Analysis for Interpretability: Learning about feature importance analysis techniques to identify the most influential features in a model's decision-making process for specific CX tasks.
Visualizing Model Behavior: Exploring techniques for visualizing model behavior, such as decision trees and partial dependence plots, to gain insights into how different features interact to influence model predictions.
Case Study 2: Utilizing feature importance analysis to understand why a customer recommendation model suggests certain products to specific customer profiles.
Guest Speaker Session: Inviting a data scientist with experience in XAI to showcase real-world applications of XAI techniques for interpreting AI models used in CX initiatives.
Hands-on Session: Applying feature importance analysis and visualization techniques to a pre-trained model (e.g., customer segmentation model) to interpret its decision-making process.
4. Communicating AI Insights & Responsible XAI Practices:
Counterfactual Explanations for Specific Customer Scenarios: Understanding counterfactual explanations, a powerful XAI technique that allows you to see how a model's prediction would change if a specific feature value were different (e.g., "What if this customer had a higher loyalty score?").
Communicating XAI Findings for Informed Decision Making: Learning how to effectively communicate the results of XAI analysis to stakeholders who may not have a technical background in AI.
Case Study 3: Utilizing counterfactual explanations to understand why a customer was denied loan approval by an AI model and identify potential mitigation strategies.
Responsible XAI Practices for CX Applications: Discussing the importance of responsible XAI practices, considering fairness, bias detection, and potential limitations of XAI methods.
Course Wrap-up & Project Presentations: Teams present a chosen CX application and outline a plan for incorporating XAI methods to improve model transparency and trust. Their plan should consider the type of XAI techniques suitable for the chosen model and how the insights will be communicated to stakeholders.
Resource Sharing: Discussing best practices and ongoing resources for staying up-to-date with Explainable AI advancements and their applications in the field of Customer Experience.
Prerequisites:
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