From the course: Learning XAI: Explainable Artificial Intelligence
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Demographic parity and its applications
From the course: Learning XAI: Explainable Artificial Intelligence
Demographic parity and its applications
- Let's imagine two groups applying for a loan. One group has a 45% acceptance rate, while the other group, despite having similar qualifications, only has a 30% acceptance rate. This disparity is exactly what demographic parity aims to address. Let's talk about how this mathematical concept can help us identify and correct unfair treatment in AI systems. Demographic parity has one simple but powerful requirement, the positive rates of the underrepresented group should equal the percentages of positive rates of the overrepresented group. In mathematical terms, this looks like, so why does all of this matter? Well, this means that outcomes should be distributed equally across all groups. If 45% of one group gets approved for something, 45% of other groups should too. We can measure this using a confusion metrics, which helps us visualize and calculate the positive rate of each subgroup. This gives us a clear, quantifiable way to see if our model is treating different groups fairly…
Contents
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Introduction to GenAI model training2m 9s
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(Locked)
Demographic parity and its applications2m 28s
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Equal opportunity parity for evaluating fairness2m 2s
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Equalized odds parity to compare subgroup performance2m 1s
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HELM2m 47s
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Red-teaming1m 49s
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Exercise: Building an evaluation pipeline9m 11s
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