AI/ML Testing Methodology

Requirement Analysis & Use Case Definition:

• Identify business objectives, success criteria, and model expectations.

• Define key performance indicators (KPIs) such as accuracy, precision, recall, F1-score, latency, and interpretability.

• Assess compliance requirements (GDPR, HIPAA, ISO, NIST).

Data Validation & Quality Assurance:

• Validate data sources for accuracy, completeness, and bias.

• Perform data integrity checks to identify duplicates, anomalies, or missing values.

• Assess data labeling quality and consistency.

• Ensure balanced datasets to avoid bias and unfair predictions.

Model Testing & Validation:

• Functional Testing – Verify that the model produces expected outputs for defined inputs.

• Regression Testing – Ensure that model updates don’t degrade performance.

• Bias & Fairness Testing – Identify and mitigate biases to ensure fairness across demographic groups.

• Adversarial Testing – Assess model resilience against adversarial attacks and data poisoning.

• Explainability & Interpretability – Validate that model decisions can be understood and justified.