In the recurring debate about bias in Machine Learning models, there is a growing argument saying that “the problem is not in the data”, often citing the influence of various choices like loss functions or network architecture. In this video, we take a look at PAIR’s AI Explorables through the lens of whether or not the bias problem is a data problem.
OUTLINE:
0:00 – Intro & Overview
1:45 – Recap: Bias in ML
4:25 – AI Explorables
5:40 – Measuring Fairness Explorable
11:00 – Hidden Bias Explorable
16:10 – Measuring Diversity Explorable
23:00 – Conclusion & Comments
AI Explorables:
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