Pipeline Safety AI Evaluator

A rigorous evaluation framework for AI systems in pipeline safety-critical applications

Version 0.1.0 | Beta

Mission Statement

PSAE provides a scientifically-validated framework for evaluating AI systems in pipeline safety-critical applications. Built on peer-reviewed methodologies from safety-critical systems research, PSAE addresses the unique challenges of assessing AI in environments where incorrect recommendations can result in catastrophic failures, environmental disasters, or loss of life.

Key Differentiators

FeatureIndustry StandardPSAE Innovation
Test CoverageNormal operations onlyNormal + abnormal + edge cases
Statistical RigorBasic accuracy metricsConfidence intervals, significance testing
Human FactorsAI-only testingHuman-AI collaborative evaluation
Real-World ValidationTheoretical scenariosPHMSA incident-based test cases
Safety WeightingEqual weight to all testsRisk-adjusted safety multipliers
ReproducibilityLimited documentationFull protocol + code + data

Evaluation Framework

PSAE evaluates AI across six weighted primary metrics:

Accuracy25%

Information correctness

Relevance20%

Domain appropriateness

Safety20%

Protocol adherence

Completeness15%

Coverage of aspects

Technical Depth10%

Engineering calculations

Sources10%

Reference utilization

Full grading methodology (risk multipliers, penalties, pass/fail) →

Dataset

Explore test cases, benchmarks, and human baseline data

Results

View model benchmarks, comparisons, and evaluation reports

Documentation

Methodology, implementation guides, and compliance standards

White Paper

Research paper alignment and methodology details