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12.1 Foundations of Controlled Experiments: Causality, Randomization Units, and the Overall Evaluation Criterion

I. Why Controlled Experiments Establish Causality

II. The Randomization Unit and Consistent Assignment

III. The Overall Evaluation Criterion

IV. How Often Ideas Work, and What to Do about Surprises

12.2 Running a Trustworthy Experiment: Statistical Power, Sample Ratio Checks, Peeking, Novelty Effects, and Interference

I. Enough Data: Statistical Power and Sample Size

II. Is the Experiment Trustworthy? Sample Ratio Mismatch

III. Peeking and Experiment Duration

IV. Novelty and Primacy Effects

V. Interference between Groups

12.3 When You Cannot Randomize: Difference-in-Differences, Staggered Rollouts, Matched Comparisons, and Falsification Tests

I. Why "Results Improved after Launch" Is Not Evidence

II. Difference-in-Differences and the Parallel Trends Assumption

III. Staggered Rollouts and Matched Comparisons

IV. Falsification Tests and Honest Uncertainty

12.4 Choosing Metrics That Reflect Business Results: Metric Layers, Leading and Lagging Indicators, Guardrails, and Goodhart's Law

I. Business Outcome Metrics and Model Metrics

II. Leading and Lagging Indicators

III. Goal Metrics and Guardrails

IV. Goodhart's Law

12.5 Evaluating Generative and Agentic Systems: Evaluation Sets, Rubric-Based Review, AI Judges, and End-to-End Task Success

I. Why Generative Outputs Need Different Evaluation

II. Evaluation Sets and Rubric-Based Review

III. AI Models as Judges

IV. Evaluating Agents: Outcomes, Not Steps

12.6 Monitoring Systems in Production: What to Watch, Alert Thresholds, Response Times, and Feedback Loops

I. What to Monitor

II. Thresholds, Alerts, and Response

III. Feedback Loops: When the System Shapes Its Own Data

IV. Detecting and Limiting Feedback Contamination

12.7 Managing the Life Cycle of an AI System: Ownership, Review Cycles, Retraining Triggers, Retirement Criteria, and the NIST AI RMF

I. From a Launch Decision to a Life Cycle

II. Ownership and Review Cycles

III. Retraining Triggers and Revalidation

IV. Retirement Criteria and Safe Retirement

V. Reference Structures: The NIST AI RMF and US Banking Guidance

12.8 Evaluation as an Organizational Capability: Reporting to Leaders, Post-implementation Reviews, Institutional Memory, and Building an Experimentation Organization

I. Why Evaluation Capability Is an Asset

II. Reporting to Senior Leaders and Boards

III. Post-implementation Reviews

IV. Institutional Memory

V. Building an Experimentation Organization

Knowledge Check

Knowledge Check:

Knowledge Check I. Why Controlled Experiments Establish Causality

Knowledge Check II. The Randomization Unit and Consistent Assignment

Knowledge Check III. The Overall Evaluation Criterion

Knowledge Check I. Enough Data: Statistical Power and Sample Size

Knowledge Check II. Is the Experiment Trustworthy? Sample Ratio Mismatch

Knowledge Check III. Peeking and Experiment Duration

Knowledge Check IV. Novelty and Primacy Effects

Knowledge Check I. Why "Results Improved after Launch" Is Not Evidence

Knowledge Check II. Difference-in-Differences and the Parallel Trends Assumption

Knowledge Check III. Staggered Rollouts and Matched Comparisons

Knowledge Check IV. Falsification Tests and Honest Uncertainty

Knowledge Check I. Business Outcome Metrics and Model Metrics

Knowledge Check III. Goal Metrics and Guardrails

Knowledge Check IV. Goodhart's Law

Knowledge Check II. Evaluation Sets and Rubric-Based Review

Knowledge Check III. AI Models as Judges

Knowledge Check IV. Evaluating Agents: Outcomes, Not Steps

Knowledge Check 12.7 Managing the Life Cycle of an AI System: Ownership, Review Cycles, Retraining Triggers, Retirement Criteria, and the NIST AI RMF

Welcome to 'Evidence and Continuous Evaluation for AI Systems,' a comprehensive course designed to equip you with the rigorous methodologies required to measure, monitor, and manage AI systems effectively. In today's rapidly evolving technological landscape, deploying an AI model is only the beginning.

Welcome to 'Evidence and Continuous Evaluation for AI Systems,' a comprehensive course designed to equip you with the rigorous methodologies required to measure, monitor, and manage AI systems effectively. In today's rapidly evolving technological landscape, deploying an AI model is only the beginning. Ensuring that it delivers genuine business value, operates safely, and adapts to changing data requires a robust framework for continuous evaluation. This course dives deep into the foundations of controlled experiments, teaching you how to establish causality, select appropriate randomization units, and define an Overall Evaluation Criterion (OEC). You will learn how to run trustworthy experiments by mastering concepts like statistical power, sample ratio mismatch, and avoiding common pitfalls like peeking and novelty effects. But what happens when you cannot randomize? We cover advanced techniques such as difference-in-differences, staggered rollouts, and falsification tests. Furthermore, you will explore how to choose metrics that truly reflect business results, navigating the complexities of Goodhart's Law and guardrail metrics. As generative AI and agentic systems become more prevalent, traditional evaluation falls short; this course provides cutting-edge strategies for rubric-based reviews, using AI models as judges, and measuring end-to-end task success. Finally, you will learn to monitor systems in production, manage the complete AI lifecycle aligned with frameworks like the NIST AI RMF, and build a lasting organizational capability for experimentation. Join us to transform how your organization evaluates and sustains AI success.

Chapter 12: Evidence and Continuous Evaluation for AI Systems

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            <p>Developing an artificial intelligence system is only the first step; proving its true business value and ensuring its long-term reliability require rigorous, ongoing evaluation. This course delves into the critical methodologies needed to establish causality and measure the real-world impact of AI deployments. By exploring the foundations of controlled experiments, you will learn how to properly define randomization units, establish an Overall Evaluation Criterion (OEC), and navigate the complexities of statistical power, sample ratio mismatches, and novelty effects. When strict randomization is not feasible, the curriculum equips you with robust alternative strategies, such as difference-in-differences, staggered rollouts, and falsification tests, ensuring you can always extract trustworthy evidence from your data.</p>

<p>Beyond traditional predictive models, the course addresses the unique challenges of evaluating modern generative and agentic AI systems through rubric-based reviews, AI-as-a-judge frameworks, and end-to-end task success metrics. You will also discover how to select metrics that genuinely reflect business outcomes, balancing leading and lagging indicators with essential guardrails to prevent pitfalls like Goodhart's Law. Finally, the curriculum transitions from initial launch to continuous production monitoring and full life cycle management. You will learn how to establish alert thresholds, manage feedback loops, define retraining triggers, and build a resilient organizational culture of experimentation and institutional memory, guided by industry standards like the NIST AI Risk Management Framework.</p>
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