Mutale Nkonde is an artificial intelligence policy analyst and a fellow at both the Berkman Klein Center for Internet & Society at Harvard University and at Stanford University’s Digital Civil Society Lab.
She is the founding president of AI For the People, a nonprofit that aims to “create the narratives needed to create an anti-racist technical future.”
Nkonde was part of the team that introduced the Algorithmic and Deep Fakes Accountability Acts as well as the No Biometric Barriers to Housing Act in the House of Representatives.
In an opinion piece for the Harvard Business Review, Nkonde argues that corporations should shoulder social responsibility for reducing race and gender bias in artificial intelligence.
She says she is currently working on a project that is looking at how black communities “will be impacted by disinformation during 2020.” She prepared a briefing sheet for journalists and spoke about the project on Sirius XM in February 2020.
Pedro Domingos teaches computer science at the University of Washington in Seattle. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning.
A Fellow of the Association for the Advancement of Artificial Intelligence, Domingos is also a winner of the SIGKDD Innovation Award, the highest honor in data science. He has held visiting positions at Stanford, Carnegie Mellon, and MIT.
Charles Isbell is Senior Associate Dean and professor at Georgia Tech’s College of Computing and an expert in artificial intelligence. To let his bio explain:
Dr. 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, Dr. 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.
Senior Associate Dean at the College of Computing, Professor of Interactive Computing, Georgia Institute of Technology
Areas of Expertise: Artificial Intelligence, Interactive Computing, Software Engineering, Computer Programming Languages