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Data Fans: learn more about hypothesis, assumptions, and bias in software

San Francisco

February 13, 2020 7:00 pm MST

Removing Unfair Bias in Machine Learning – Upkar Lidder

Extensive evidence has shown that AI can embed human and societal bias and deploy them at scale. And many algorithms are now being reexamined due to illegal bias. So how do you remove bias & discrimination in the machine learning pipeline?

In this talk you’ll learn the de-biasing techniques that can be implemented by using the open source toolkit AI Fairness 360. This toolkit is the first solution that brings together the most widely used bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia. You’ll take an introductory look at how bias & discrimination can arise within modern machine learning techniques and the methods that can be implemented to tackle those challenges. Learn how to evaluate the metrics using the open-source AI Fairness 360 Toolkit to check for fairness and mitigate machine learning model bias.


Upkar Lidder is a Full Stack Developer and Data Wrangler at IBM with a decade of development experience in a variety of roles. He can be seen speaking at various conferences and participating in local tech groups and meetups. He is currently curious about magic behind Machine Learning and Deep Learning. Upkar went to graduate school in Canada and currently resides in the United States.

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