Building Ethics and Diversity in AI
Bias in machine learning is a significant concern as technology gets increasingly ubiquitous across many industries. Some types of bias can be attributed to limits in design and tooling; however, the bias in the training data itself is a general phenomenon. Skewed training data propagates into discriminatory AI models that amplify human prejudices.
Building a data labeling framework that uses a diverse set of crowd workers to collect and label the data can help reduce bias.
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