Module 4: Evaluating AI Models with Rubrics – Mastering Rubrics for AI Training & Evaluation

Module 4 Overview: Evaluating AI Models with Rubrics

Prerequisites:

• Module 1: Introduction to AI Training and Evaluation
• Module 2: Rubric Fundamentals
• Module 3: Creating Rubrics for AI Training

📌 Module Overview

In this module, we focus on how rubrics can be applied to assess AI models after training. Just as rubrics help structure AI training, they also play a critical role in evaluating model performance, reliability, and fairness.

By the end of this module, you will:
✅ Understand how rubrics can measure model accuracy, robustness, and generalization.
✅ Learn how to design evaluation rubrics for different AI models and tasks.
✅ Explore bias detection and fairness rubrics to ensure ethical AI.
✅ Gain insights into real-world case studies of AI model evaluation using structured assessment techniques.

📖 Topics Covered in This Module

📌 Topic 7: Performance Evaluation Rubrics

• Lesson 19: Designing Rubrics for Model Accuracy
  • Learn how to assess model accuracy based on different performance metrics.
• Lesson 20: Evaluating Model Robustness
  • Understand how rubrics can test how well AI models handle edge cases and adversarial examples.
• Lesson 21: Assessing Generalization Ability
  • Develop techniques to evaluate how AI models perform on unseen data.

📌 Topic 8: Ethical Considerations in AI Evaluation

• Lesson 22: Fairness and Bias in AI
  • Identify bias in AI models and use rubrics to reduce discrimination in model outcomes.
• Lesson 23: Transparency and Accountability
  • Learn how structured evaluations improve trust in AI decisions and promote responsible AI.
• Lesson 24: Privacy Concerns in AI
  • Understand how rubrics help evaluate AI models handling sensitive data ethically.

📌 End-of-Module Quiz

• Quiz 4: Evaluating AI Models
  • Reinforce key learning concepts through challenging multiple-choice and true/false questions.