From the course: AI Accountability: Build Responsible and Transparent Systems (2022)
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Construct validity
From the course: AI Accountability: Build Responsible and Transparent Systems (2022)
Construct validity
- One of the things about the COVID-19 pandemic is that it was a shared traumatic experience in that it pretty much turned everybody's life upside down in one way or another. One of the more perplexing issues about the pandemic was assessing its effect on the, quote, economy. Millions of people lost their jobs, but, strangely, after an initial dip, the stock market did great. Here in the US, it took the S&P 500 less than five months to return to pre-pandemic levels. If you had money in stocks, things were fabulous. If you made your money by showing up somewhere in person and working, chances are it wasn't so fabulous. So when people talk about the economy, they should be clear what they're talking about, stock prices or people who have jobs and earn money. They're different things. And this gets to the issue of constructs and construct validity. A construct is an abstract thing that can only be measured indirectly.…
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Contents
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The challenge of classification errors3m 39s
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(Locked)
The causes of classification errors6m 18s
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(Locked)
Bias in AI3m 50s
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(Locked)
Supervised and unsupervised learning8m 16s
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(Locked)
Biased labeling of data7m 15s
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Construct validity6m 14s
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(Locked)
The absence of meaning4m 54s
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Vulnerability to attacks4m 45s
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