Machine Learning Testing Ecosystem of Python
summary
Shows concrete vulnerabilities—adversarial examples, membership inference, distribution shift and bias—and names Python toolkits that test them: ART, CleverHans, AI Fairness and differential privacy techniques. Points out regulators (European Commission proposal) requiring test reports and human‑in‑the‑loop monitoring for high‑risk systems. Describes an end‑to‑end validator being developed that runs configurable tests in a CI/CD style, versions models, blocks deployments when thresholds fail, and integrates testing with monitoring; defensive success depends on adversarial training and tradeoffs in accuracy and cost.
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chapters (25)
- 0:00Intro
- 0:31Vulnerabilities of ML
- 2:18Evasion Attacks
- 6:55Physical Attacks
- 7:53Biased decisions
- 9:20Change in data distributions
- 10:47Al Incident Database
- 11:39ML Red and Blue Teams Wanted!
- 15:24Definiton of HRAI Systems
- 16:39Regulations
- 17:57Classical Machine Learning Pipeline
- 18:36This is where we're going
- 19:32RMS in the ML. Pipeline
- 21:14Four Pillars of Trustworthy AI
- 21:43General Remarks
- 23:33Security
- 26:19Transferability of Attacks
- 28:54Membership Inference Attacks
- 29:27Model Inversion Attacks
- 30:12Privacy: Tools for testing
- 30:26Fairness: Tools for testing
- 31:32What to do?
- 33:31Validator QMS
- 34:04Data drift and fairness tests
- 34:31Some Guiding Principles
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