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1
Intro
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What do you do
3
Whats the interest
4
Whats the fear
5
What can go wrong
6
Build your own AI
7
Bias
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No production
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Confounding factors
10
Semantic gap
11
Bias fairness
12
Machine learning systems
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Unexpected behavior
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Data poisoning
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adversarial examples
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robust attacks
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stickers on objects
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curse of dimensionality
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other examples
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good AI systems
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phishing attempts
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social media
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Phishing
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LinkedIn
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Fake news
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Fake images
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Fake text
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Recommendation systems
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YouTube
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Conspiration theories
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YouTube recommendation system
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Fake hoaxes
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Explainable AI
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Scams
35
Awareness
36
GDPR
37
Policymakers
38
The EU
39
Smallboots
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the potential pitfalls and dangers of AI systems in autonomous decision-making through this insightful conference talk. Delve into critical issues such as bias, fairness, confounding variables, adversarial attacks, ethics, and explainability. Gain a high-level understanding of the security and privacy concerns surrounding AI applications for individuals and society. Learn from computer scientist Joachim Ganseman as he shares his expertise on topics including natural language processing, conversational interfaces, and their applications in government and social security administration. Discover the implications of AI in various contexts, from phishing attempts and fake news to recommendation systems and conspiracy theories. Examine the importance of explainable AI, awareness, and policy-making in addressing these challenges, with a focus on European Union initiatives.

Some Pitfalls in AI

Devoxx
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