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\nAlgorithmic Bias and Fairness\n

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\n\nPublisher:\n\nCrash Course\n\n\n
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\nToday, 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...\nToday, 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. \n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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10:56
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Grade
12th - Higher Ed
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Subjects
Math
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Media Type
Instructional Videos
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Source:
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\nCrash Course Artificial Intelligence\n\n
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Date
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2019
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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neighborhoods, algorithms, bias, data, police, discrimination, society, gender, john green, patterns, artificial intelligence, computer science, feedback loops
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Additional Tags

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unbalanced classes, robot, biases, features, robots weekly, algorithmic bias, photo, people, strongly correlated, groups, hidden biases, malicious data, good grades, fed back, protected class, recommendations, training data, machine learning, ai, ai systems, good, pay attention, make, ai algorithms, future, crash course, race
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Classroom Considerations

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\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Algorithmic Bias and Fairness\" href=\"/subscription/new\">\"Algorithmic\n\n
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