AI applications are ubiquitous – and so is their potential to exhibit unintended bias. Algorithmic and automation biases and algorithm aversion all plague the human-AI partnership, eroding trust between people and machines that learn.

But can bias be eradicated from AI?

AI systems learn to make decisions based on training data, which can include biased human decisions and reflect historical or social inequities, resulting in algorithmic bias. The situation is exacerbated when employees uncritically accept the decisions made by their artificial partners. Equally problematic is when workers categorically mistrust these decisions.

Join our panel of industry and academic leaders, who will share their technological, legal, organizational and social expertise to answer the questions raised by emerging artificial intelligence capabilities.

Moderator:
Dr Fay Cobb Payton is a Professor of Information Systems & Technology at NC State’s Poole College of Management and a Program Director at the National Science Foundation in the Division of Computer and Network Systems

Panelists:
Timnit Gebru- Research scientist and the co-lead of the Ethical AI team at Google and the co-founder of Black in AI, a place for fostering collaborations to increase the presence of Black people in the field of Artificial Intelligence

Brenda Leong- Senior Counsel and Director of Artificial Intelligence and Ethics at the Future of Privacy Forum

Professor Mohammad Jarrahi- Associate Professor at UNC’s School of Information and Library Science focused on the intersection of technology and society

Chris Wicher- Rethinc. Labs AI Research Fellow, former Director of AI Research at KPMG’s AI Center of Excellence and Vice President of Watson Engineering at IBM heading up the engineering and delivery of the first commercial IBM Watson system