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Classroom Contents
Some Pitfalls in AI
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- 1 Intro
- 2 What do you do
- 3 Whats the interest
- 4 Whats the fear
- 5 What can go wrong
- 6 Build your own AI
- 7 Bias
- 8 No production
- 9 Confounding factors
- 10 Semantic gap
- 11 Bias fairness
- 12 Machine learning systems
- 13 Unexpected behavior
- 14 Data poisoning
- 15 adversarial examples
- 16 robust attacks
- 17 stickers on objects
- 18 curse of dimensionality
- 19 other examples
- 20 good AI systems
- 21 phishing attempts
- 22 social media
- 23 Phishing
- 24 LinkedIn
- 25 Fake news
- 26 Fake images
- 27 Fake text
- 28 Recommendation systems
- 29 YouTube
- 30 Conspiration theories
- 31 YouTube recommendation system
- 32 Fake hoaxes
- 33 Explainable AI
- 34 Scams
- 35 Awareness
- 36 GDPR
- 37 Policymakers
- 38 The EU
- 39 Smallboots
