// Abstract

Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements as well as issues with datasets that they're typically trained on.

In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling!

// Bio

Vincent D. Warmerdam is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. He's especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has a preference for simpler solutions that scale, as opposed to the latest and greatest from the hype cycle. He currently works as a Research Advocate at Rasa where he collaborates with the research team to explain and understand conversational systems better.

Outside of Rasa, Vincent is also well known for his open-source projects (scikit-lego, human-learn, doubtlab, and more), collaborations with open source projects like spaCy, his blog over at koaning.io, and his calm code educational project.

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