Let’s Talk about Bias and Diversity in Data, Software, and Institutions

Bias and lack of diversity have long been deep-rooted problems across industries. To discuss how these issues impact data, software, and institutions, and how we can improve moving forward, the Machine Learning Center at Georgia Tech (ML@GT) will be hosting a panel discussion on Friday, Nov. 20 from 12-1 pm ET.

The panel will feature thought leaders from Google, Georgia Tech, and Queer in AI, who will together answer questions like "What implications and problems exist or will exist if the tech workforce does not become more diverse?" and "How does anyone make sure they are not introducing their bias into a given system? What questions should we be asking or actions should we be taking to avoid this?"

Registration is required and available here.

Panelists

Charles Isbell

Charles Isbell is the Dean of Computing and The John P. Imlay Jr. Chair at Georgia Tech’s College of Computing. He is also a professor in the School of Interactive Computing and the Machine Learning Center at Georgia Tech (ML@GT.)

Isbell's research passion is artificial intelligence. In particular, he focuses on applying statistical machine learning to building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human.

Lately, Isbell has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition); scalable coordination; and development environments that support the rapid prototyping of adaptive agents. As a result, he has begun developing adaptive programming languages, worrying about issues of software engineering, and trying to understand what it means to bring machine learning tools to non-expert authors, designers, and developers.

Rapha Gontijo Lopes

Rapha Gontijo Lopes is a research associate at Google Brain and a founder of Queer in AI. He joined Google as an AI Resident in the summer of 2018 after completing his B.S. degree in Computer Science at Georgia Tech. His work investigates how deep learning works (or doesn't), and the role that input distributions have on model robustness. As an organizer for Queer in AI, he creates academic workshops that drive the research conversation at the intersection of AI and LGBTQ+ people.

Tiffany Deng

Tiffany Deng leads the Responsible AI Program Management Team at Google where she is focused on helping people build products that work for everyone. Prior to Google, Tiffany worked as a Privacy Program Manager at Facebook and as consultant in Washington, D.C.

Moderator: Deven Desai

Deven Desai is an associate director of the Machine Learning Center (ML@GT) and associate professor in the Scheller College of Business. He was also the first, and to date, only Academic Research Counsel at Google, Inc., and a Visiting Fellow at Princeton University's Center for Information Technology Policy.

 Professor Desai's scholarship examines how business interests, new technology, and economic theories shape privacy and intellectual property law and where those arguments explain productivity or where they fail to capture society's interest in the free flow of information and development