Crash Course Artificial Intelligence - Season 1 Episode 18 Algorithmic Bias and Fairness
2019-12-27
48 minutes.
Season - Episode
1
Season 1 Aug 09, 2019
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1 - 1What Is Artificial Intelligence? Aug 09, 2019 -
1 - 2Supervised Learning Aug 16, 2019
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1 - 3Neural Networks and Deep Learning Aug 23, 2019
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1 - 4Training Neural Networks Aug 30, 2019
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1 - 5How to make an AI read your handwriting (LAB) Sep 06, 2019
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1 - 6Unsupervised Learning Sep 20, 2019
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1 - 7Natural Language Processing Sep 27, 2019
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1 - 8Make an AI sound like a YouTuber (LAB) Oct 04, 2019
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1 - 9Reinforcement Learning Oct 11, 2019
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1 - 10Symbolic AI Oct 18, 2019
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1 - 11Robotics Oct 25, 2019
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1 - 12AI Playing Games Nov 01, 2019
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1 - 13Let's make an AI that destroys video games Nov 08, 2019
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1 - 14Humans and AI working together Nov 15, 2019
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1 - 15How YouTube knows what you should watch Nov 22, 2019
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1 - 16Let’s make a movie recommendation system Nov 29, 2019
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1 - 17Web Search Dec 06, 2019
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1 - 18Algorithmic Bias and Fairness Dec 13, 2019
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1 - 19Cats Vs Dogs? Let's make an AI to settle this Dec 20, 2019
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1 - 20The Future of Artificial Intelligence Dec 27, 2019
0
Season 0 Aug 02, 2019
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0 - 1Crash Course Artificial Intelligence Preview Aug 02, 2019
Overview
Today, we're going to talk about five common types of algorithmic bias we should pay attention to: data that reflects existing biases, unbalanced classes in training data, data that doesn't capture the right value, data that is amplified by feedback loops, and malicious data. Now bias itself isn't necessarily a terrible thing, our brains often use it to take shortcuts by finding patterns, but bias can become a problem if we don't acknowledge exceptions to patterns or if we allow it to discriminate.
Year 2019
Studio
Director
Popularity 0.344
Language English
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