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Artificial Intelligence (AI) Ethics and Bias Mitigation

Class Details

The increasingly widespread use of Natural Language Processing (NLP) in AI applications must be continually monitored for biases and false associations, especially those surrounding protected or disadvantaged classes of people. This webinar will discuss methods used to mitigate such biases and their weak points, using real world examples in civilian agencies. This webinar will be presented by Melanie Laffin.

Speaker: Melanie Laffin wants a world where robots do all the boring, repetitive stuff for her so she can spend her time doing not boring, repetitive stuff. As a mathematician-turned-programmer with more than a decade of experience, she has worked at universities, PwC, Capgemini and Booz Allen analyzing data, architecting robots, producing cool demonstrations of artificial intelligence, discovering (inventing?) mathematics, managing large technical implementations, and trying to keep people from freaking out.

MLA and CHIS credits are not available for this webinar.

Registration closes at 5:00pm ET on Mon, Feb 22.

Class Date:
Region/Office: National
Feb 23, 2021
3:00PM - 4:00PM ET
Instructor(s):
Margot G. Malachowski, MLS, AHIP, Education and Outreach Coordinator
Continuing Education Credits: 
0
This class is now closed to registrants.

The increasingly widespread use of Natural Language Processing (NLP) in AI applications must be continually monitored for biases and false associations, especially those surrounding protected or disadvantaged classes of people. This webinar will discuss methods used to mitigate such biases and their weak points, using real world examples in civilian agencies. This webinar will be presented by Melanie Laffin.

Speaker:  Melanie Laffin wants a world where robots do all the boring, repetitive stuff for her so she can spend her time doing not boring, repetitive stuff. As a mathematician-turned-programmer with more than a decade of experience, she has worked at universities, PwC, Capgemini and Booz Allen analyzing data, architecting robots, producing cool demonstrations of artificial intelligence, discovering (inventing?) mathematics, managing large technical implementations, and trying to keep people from freaking out. 

MLA and CHIS credits are not available for this webinar.