Human bias in machines | AI career development series

Come join us for a little preview of the upcoming AI + Humans Conference where we will feature a full day of Social Good uses cases across such topics as Privacy, Personal Safety, Public Policy, Inclusion, Employment, Education, Legal Justice, Conflict and others. We also will be featuring more career development sessions during the conference

>>Sign up for full day October 25th , AI for Social Good Conference here:
https://www.chicagoaidays.com/registration/ai-social-good

>> Attend this , September 26th AI + Humans preview for FREE.

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Hear form Joan Wang, of Omelas, who will share her professional journey as an up and coming AI Leader. She will also present on Bias in AI,.

AGENDA

4:00PM – 4:30PM Networking
4:30PM – 5:00PM AI + Career Interview
5:00PM – 5:45PM AI + Social Good Presentation
5:45PM – 6:30PM Networking

CAREER INTERVIEW

This talk is part of our Women in AI Leadership series that features successful woman in the AI field. Our featured speakers are living proof of diversity in AI and have found ways to move forward in the space.. We hope their stories can help give insights into how we could build further diversity in the local AI and data science community.

TALK : HUMAN BIASES IN MACHINES

Bias which is one of the fundamental flashpoints in ensuring all AI applications are engineered for the good of all as opposed to unevenly.

Description: AI and machine learning models are man-made systems that reflect the human societies in which they are created -- this includes the biases ingrained in those societies. In this talk, we will explore human biases in machine learning. What do we mean by bias and fairness? What are the sources of bias? How do we evaluate and mitigate it? This discussion will help to move us towards designing models that are more mindful and inclusive.

SPEAKER BIO

Joan Wang is a Senior Data Scientist at Omelas, where she builds and deploys machine learning models that help make sense of the online narratives shared by strategic state and nonstate actors. She previously conducted research with the Urban Institute to explore the connection between physical and digital segregation in Chicago. Prior to entering the field of data science and public policy, Joan worked in client services at PwC Forensics Advisory. Joan received her M.S. in Computational Analysis and Public Policy from the University of Chicago, and was awarded an inaugural Siebel Scholarship in Computer Science. She completed her undergraduate studies at the University of California, Berkeley, with B.A.'s in Economics and Operations Research & Management Science.