From the course: Data Science Foundations: Fundamentals
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Data ethics
From the course: Data Science Foundations: Fundamentals
Data ethics
- [Instructor] Let's start by stating the obvious. If your data project ends with everybody screaming at you, then something has gone wrong. And when it comes to data ethics, things can go very wrong very quickly. Now, let me give you just a different perspective for a moment, my background is in academic research. I have a PhD in social psychology, which is a research field, and I'm a tenured full-time professor at a university. Whenever an academic does research with people, there were a few general principles that apply. For instance, it is absolutely mandatory to have voluntary participation, that is, people need to choose freely, without coercion, to participate in your study. Second, absolutely mandatory, there needs to be informed consent. People must know what they're getting into so they can make a free, voluntary choice to participate. Now, two other things that are not necessarily mandatory, but you got to…
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Contents
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Data preparation5m 26s
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(Locked)
Labeling data8m 48s
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(Locked)
In-house data5m 38s
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(Locked)
Open data4m 15s
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(Locked)
APIs2m 40s
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(Locked)
Scraping data4m 44s
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(Locked)
Creating data5m 36s
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Passive collection of training data3m 57s
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(Locked)
Self-generated data3m 30s
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(Locked)
Data vendors5m 30s
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(Locked)
Data ethics5m 14s
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