Today we’re joined by Hal Daumé III, professor at the University of Maryland, Senior Principal Researcher at Microsoft Research, and Co-Chair of the 2020 ICML Conference.
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We had the pleasure of catching up with Hal ahead of this year’s ICML to discuss his research at the intersection of bias, fairness, NLP, and the effects language has on machine learning models. We explore language in two categories as they appear in machine learning models and systems: (1) How we use language to interact with the world, and (2) how we “do” language. We also discuss ways to better incorporate domain experts into ML system development, and Hal’s experience as ICML Co-Chair.
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Connect with Hal!
- Paper: Paper: Language (technology) is Power: A Critical Survey of “Bias” in NLP
- Paper: Improving fairness in machine learning systems: What do industry practitioners need?
- Paper: Toward Gender-Inclusive Coreference Resolution
- #232 – Fairness in Machine Learning with Hanna Wallach
- #219 – Legal and Policy Implications of Model Interpretability with Solon Barocas
- #376 – Is Linguistics Missing from NLP Research? w/ Emily M. Bender
- Ethics, Bias, and Fairness in AI – A TWIML Playlist
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- Check out our TWIML Presents: series page!
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- Check out the official TWIMLcon:AI Platform video packages here!
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“More On That Later” by Lee Rosevere licensed under CC By 4.0