Scrutinizing AI Models to Reduce Unintended Bias | Research

Today's artificial intelligence models are revolutionary — reshaping industries by automating complex business decisions previously left for experts. Success with AI, as many now know, is predicated on data. So, what happens when the data we feed our AI models is inherently biased?

Join WWT Data Scientist Charlene Ulrich and Big Data Consultant Daniel Cholakov as they talk about the growing need for leaders to scrutinize AI and natural language processing (NLP) models for bias. Charlene and Daniel use a recent WWT Research paper on mitigating bias in AI using debias-GAN as a jumping off point for a conversation that includes why de-biasing AI is important, where and how bias can take place and how you can use our findings to to improve your AI strategies.

What to expect

WWT Research is a recurring webinar series that highlights WWT's in-depth research reports that analyze the latest technology and industry trends, featuring guests from WWT's extensive roster of subject matter experts and technologists. Hosted by Robb Boyd, episodes are published exclusively for registered users of the ATC Platform. Registration also grants users free access to our WWT Research reports, 24/7 access to our virtual lab and training environments and vast library of technical and business-oriented content. Use our platform to:

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Goals and Objectives

Gain clarity on the important issue of bias in AI, understand how to quantify bias in AI, learn about WWT's latest research on the topic and leave with steps on how to bake fairness into your AI strategies.

Who should attend?

Data scientists, engineers, and consultants looking to stay ahead of the curve in AI and ML technologies.