This talk was presented at PyBay2019 - 4th annual Bay Area Regional Python conference. See pybay.com for more details about PyBay and click SHOW MORE for more information about this talk.
Description Through a series of case studies, I will illustrate different types of algorithmic bias, debunk common misconceptions, and share steps towards addressing the problem.
Original slides: https://t.ly/9gO5k
About the speaker Rachel Thomas is a professor at the University of San Francisco Data Institute and co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 200,000 students have taken and which has been featured in The Economist, MIT Tech Review, and Forbes. She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker. In her TEDx talk, she shares what scares her about AI and why we need people from all backgrounds involved with AI.
Sponsor Acknowledgement This and other PyBay2019 videos are via the help of our media partner AlphaVoice (https://www.alphavoice.io/)!
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