Building Ethics and Diversity in AI

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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.

Featured Presenters

Meeta Dash

VP of Product at Appen As VP Product at Appen Meeta is building a machine learning data annotation platform focused on Computer Vision, Autonomous Vehicles,

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