Removing Unfair Bias in Machine Learning Webcast Recording + Slides

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Removing Unfair Bias in Machine Learning Webcast Recording + Slides 

Wed November 13, 2019 04:47 PM

IBM Webcast Summary

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 webinar you'll learn the debiasing techniques that can be implemented by using the open source toolkit AI Fairness 360.

AI Fairness 360 (AIF360) is an extensible, open source toolkit for measuring, understanding, and removing AI bias. AIF360 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.

In this webinar you'll learn:

  • How to measure bias in your data sets & models
  • How to apply the fairness algorithms to reduce bias
  • How to apply a practical use case of bias measurement & mitigation in a data-driven medical care management scenario.
Trisha Mahoney
Trisha Mahoney
Senior AI Tech Evangelist

Trisha Mahoney is an AI Tech Evangelist for IBM with a focus on Fairness & Bias. Trisha has spent the last 10 years working on Artificial Intelligence and Cloud solutions at several Bay Area tech firms including (Salesforce, IBM, Cisco). Prior to that, Trisha spent 8 years working as a data scientist in the chemical detection space. She holds an Electrical Engineering degree and an MBA in Technology Management.

Karthikeyan Natesan Ramamurthy
Karthikeyan Natesan Ramamurthy
Research Staff Member
IBM Research AI

Karthikeyan Natesan Ramamurthy is a research staff member in IBM Research AI at the T. J. Watson Research Center. His broad research interests include understanding the geometry and topology of high-dimensional data and developing theory and methods for efficiently modeling the data. He has also been intrigued by the interplay between humans, machines, and data and the societal implications of machine learning. His papers have won best paper awards at the 2015 IEEE International Conference on Data Science and Advanced Analytics and the 2015 SIAM International Conference on Data Mining. He's an associate editor of Digital Signal Processing and a member of the IEEE. He holds a PhD in electrical engineering from Arizona State University.



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