VIP20

本课程属于：035-AI for CAE Simulation GNN and VVUQ English Technical Courses · 共 20 门相关课程，组合学习更完整。

查看所属VIP全部课程 →

← 返回所属目录咨询 / 购买

AI Model Verification Validation and Credibility Assessment

30 Chapters

View Course Samples Online

Foundations of AI model credibility

definitions of verification, validation, and credibility assessment; distinguishing V&V from testing and assurance; regulatory and ethical drivers.

Foundationscredibility

AI model lifecycle and V&V integration

requirements analysis, design, data collection, training, deployment, monitoring; where V&V activities fit and how they interact with MLOps.

lifecycleintegration

Verification techniques for model implementation

static analysis, formal methods, symbolic execution, and runtime verification applied to AI code and model artifacts.

Verificationtechniques

Validation fundamentals

intended use specification, operational domain definition, and acceptance criteria; linking validation to model purpose and context of use.

Validationintended

Data quality and representativeness assessment

data provenance, completeness, label accuracy, bias detection, and coverage analysis for training and test datasets.

representativenessassessment

Model performance evaluation

metrics selection, cross-validation, holdout sets, and statistical significance; pitfalls of overfitting and data leakage.

performanceevaluation

Robustness testing

adversarial examples, distribution shift, corruption benchmarks, and stress testing; methods to quantify and improve robustness.

Robustnessadversarial

Uncertainty quantification and calibration

aleatoric and epistemic uncertainty, Bayesian methods, ensemble approaches, and calibration metrics for credible predictions.

Uncertaintyquantification

Explainability and interpretability in V&V

local and global explanation methods, faithfulness evaluation, and using explanations to support validation evidence.

Explainabilityinterpretability

Fairness and bias assessment

defining fairness metrics, bias detection across subgroups, mitigation strategies, and fairness validation reporting.

Fairnessassessment

Safety and hazard analysis for AI systems

hazard identification, risk assessment, STPA, FMEA, and safety case patterns for machine learning components.

analysisidentification

Security verification of AI models

threat modeling, adversarial robustness, backdoor detection, privacy leakage testing, and secure deployment verification.

Securityverification

Formal verification and specification of AI models

property specification, reachability analysis, abstract interpretation, and verification of neural network properties.

verificationspecification

Testing strategies for AI systems

unit, integration, system, and acceptance testing; metamorphic testing, differential testing, and fuzz testing for ML.

strategiesintegration

Simulation-based validation and digital twins

scenario generation, sim-to-real gap assessment, and validation in virtual environments for autonomous systems.

Simulation-basedvalidation

Human-in-the-loop validation

user studies, human-AI team performance evaluation, and incorporating human factors into credibility assessment.

Human-in-the-loopvalidation

Statistical validation and confidence estimation

hypothesis testing, confidence intervals, power analysis, and sequential testing for model acceptance.

Statisticalvalidation

Credibility assessment frameworks

overview of standards and guidelines (e.g., FDA, ISO/IEC, NIST AI RMF), and how to structure a credibility assessment plan.

Credibilityassessment

Evidence generation and documentation

building a V&V evidence portfolio, traceability matrices, and model cards for credibility reporting.

Evidencegeneration

Independent review and audit

third-party assessment, red teaming, audit trails, and reproducibility requirements for credible AI.

Independentthird-party

Continuous validation and monitoring

drift detection, performance monitoring, feedback loops, and revalidation triggers in production.

Continuousvalidation

V&V for a medical imaging diagnostic model; applying validation methods, regulatory considerations, and credibility evidence.

diagnosticapplying

verification and validation of an autonomous driving perception system; simulation, scenario-based testing, and safety case development.

verificationvalidation

credibility assessment of a natural language processing model in a financial advisory context; fairness, robustness, and explainability.

credibilityassessment

Tooling for AI V&V

overview of open-source and commercial tools for testing, robustness, fairness, explainability, and formal verification.

open-sourcecommercial

Practical engineering task

designing a V&V plan for a given AI model; defining scope, methods, metrics, and acceptance criteria.

designingdefining

Practical engineering task

conducting a data quality and bias audit on a provided dataset; documenting findings and mitigation recommendations.

conductingprovided

Practical engineering task

performing robustness and uncertainty evaluation of a trained model; generating an evidence report.

performingrobustness

Practical engineering task

building a credibility assessment report using a selected framework; integrating evidence and traceability.

buildingcredibility

Future directions and emerging challenges in AI V&V

foundation models, generative AI, continual learning, and evolving regulatory landscapes.

directionsemerging