Some pitfalls in AI
summary
Checks common deployment failures: training data inherit human bias so Amazon scraps its recruiting model after it preferentially downgrades women, and interactive systems get poisoned so Tay turns racist in 24 hours when live-trained on Twitter. Models optimize the wrong goal — robots that “jump” grow a limb to meet a height target — and classifiers remain fragile to tiny edits so sticker on stop sign gets read as beer or a patterned shirt makes surveillance miss a person. Law pushes back: GDPR limits fully automated decisions.
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chapters (39)
- 0:00Intro
- 1:01What do you do
- 1:40Whats the interest
- 2:31Whats the fear
- 4:38What can go wrong
- 6:34Build your own AI
- 6:51Bias
- 12:46No production
- 13:18Confounding factors
- 14:33Semantic gap
- 15:55Bias fairness
- 18:01Machine learning systems
- 19:58Unexpected behavior
- 20:48Data poisoning
- 23:02adversarial examples
- 24:32robust attacks
- 25:26stickers on objects
- 25:59curse of dimensionality
- 27:00other examples
- 27:53good AI systems
- 29:05phishing attempts
- 30:13social media
- 31:01Phishing
- 32:33LinkedIn
- 33:10Fake news
- 35:11Fake images
- 36:14Fake text
- 38:50Recommendation systems
- 39:32YouTube
- 40:19Conspiration theories
- 40:51YouTube recommendation system
- 41:44Fake hoaxes
- 43:45Explainable AI
- 45:15Scams
- 45:31Awareness
- 47:53GDPR
- 49:33Policymakers
- 50:12The EU
- 51:04Smallboots
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