Stochasticity And Calibration Analysis For Model Reliability Assessment

AI testing for quality, safety, compliance — vulnerability scanning, bias/toxicity detection.

Unique: Detects both stochasticity (output inconsistency) and calibration issues (confidence miscalibration) through repeated model runs and statistical analysis, enabling reliability assessment beyond single-run evaluation. The framework provides per-sample inconsistency detection rather than aggregate statistics.

vs others: More comprehensive than single-run evaluation because it detects non-deterministic behavior and calibration issues that only appear across multiple runs, rather than assuming deterministic behavior from a single evaluation.