Scenario-Based Evaluation in AI Systems
artificial-intelligence-ai.
Course Modules:
Module 1: What Is Scenario-Based Evaluation?
Why traditional metrics aren’t always enough
Real-world testing vs. lab testing
Principles of contextual evaluation
Module 2: Building Evaluation Scenarios
Defining business contexts and constraints
Identifying edge cases, rare events, and risk factors
Creating realistic user and system journeys
Module 3: Designing Multi-Metric Evaluation Plans
Combining accuracy, precision, and fairness
Trade-offs between performance and explainability
Cost-sensitive and risk-aware evaluations
Module 4: Simulating Business Impact
Estimating real-world consequences of model decisions
Cost-benefit analysis and KPI alignment
Evaluating models from stakeholder perspectives
Module 5: Error Analysis and Bias Detection
Breaking down false positives and false negatives
Detecting and addressing algorithmic bias
Evaluating model behavior on underrepresented groups
Module 6: Communicating Evaluation Results
Creating scenario reports for business and technical teams
Visualizing outcomes and decisions
Presenting recommendations for improvement or redesign
Module 7: Capstone – Evaluate a Real-World AI Model
Select a case study (e.g., fraud detection, hiring AI, healthcare diagnostics)
Define scenarios and simulate outcomes
Submit your analysis report and stakeholder summary
Tools & Technologies Used:
Python (for modeling and analysis)
Confusion matrix, SHAP, LIME
Google Colab / Jupyter
Optional: Tableau or Power BI for visualization
Target Audience:
AI practitioners aiming to build trustworthy systems
Product and project managers assessing AI fit
Data scientists improving model impact
QA teams validating model behavior before deployment
Global Learning Benefits:
Move beyond surface metrics and test AI in real-world situations
Build more inclusive, ethical, and risk-aware AI models
Align AI evaluation with business outcomes and user needs
Strengthen stakeholder confidence through scenario-backed analysis
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