AI Safety Testing Methodology

Plan Comprehensive Safety Tests

Design a testing program covering all critical safety dimensions:

Test Categories

• Functional Testing: Verify system performs intended function correctly on typical inputs
• Edge Case Testing: Test boundary conditions, extreme values, unusual combinations
• Bias and Fairness Testing: Evaluate performance across demographic groups, identify discrimination
• Robustness Testing: Test system resilience to distribution shift, corrupted inputs, noisy data
• Security Testing: Identify vulnerabilities to unauthorized access, data manipulation
• Performance Testing: Verify latency, throughput, resource usage meet requirements

Test Planning Document

• Define testing objectives and success criteria for each dimension
• Specify test data sources and generation procedures
• Document test execution procedures and responsible teams
• Establish reporting templates and metrics
• Create remediation procedures for failures

Conduct Red Teaming and Adversarial Testing

Red teaming simulates malicious actors attempting to break your system:

• Red Team Composition: Security experts, engineers unfamiliar with system, external consultants
• Attack Scenarios: Develop realistic attack profiles (e.g., disgruntled employee, nation-state actor)
• Adversarial Input Generation: Systematically craft inputs designed to trigger failures
• Data Poisoning: Test system robustness if training data is contaminated
• Model Extraction: Attempt to reverse-engineer model behavior
• Prompt Injection: For LLM systems, test susceptibility to prompt manipulation

Document all attacks, impacts, and whether system detected/prevented them. Track whether system failed safely or catastrophically.

Execute Robustness and Stress Testing

Evaluate system behavior under extreme conditions:

Robustness Testing

• Test with corrupted inputs (noise injection, missing data, format errors)
• Test with out-of-distribution data your model will encounter in production
• Test with adversarially perturbed inputs (small, imperceptible changes that fool models)
• Test with inputs from different demographic groups to ensure equitable performance
• Measure performance degradation - acceptable level depends on application risk

Stress Testing

• Test with high volume of requests - does throughput degrade? Does system crash?
• Test with long execution times - do timeouts trigger? Do circuit breakers work?
• Test with memory constraints - is memory managed properly?
• Test with disk full, network failures - does graceful degradation occur?

Conduct Ethical and Bias Assessment

Comprehensive testing of fairness and ethical implications:

• Fairness Metrics: Calculate disparate impact, demographic parity, equalized odds across groups
• Explainability Testing: Verify explanations are accurate, meaningful, useful to end users
• Transparency Assessment: Ensure users understand AI involvement, confidence levels, limitations
• Autonomy Testing: Verify humans can override AI decisions, understand when to intervene
• Stakeholder Feedback: Conduct user testing with representatives from affected communities

Analyze Results and Document Findings

Comprehensively document all testing results: