Kay Firth-Butterfield | Head Artificial Intelligence and Machine Learning
World Economic Forum

Kay Firth-Butterfield, Head Artificial Intelligence and Machine Learning, World Economic Forum

Kay Firth-Butterfield is a Barrister and part-time Judge who has worked as a mediator, arbitrator, business owner and professor in the United Kingdom. In the United States, she is the Head of AI and Machine Learning at the World Economic Forum, based in San Francisco. She is a Founding Advocate of AI-Global which is a non-profit dedicated to the exploring and creating laws and ethics around the development and use of AI and to the socially beneficial use of AI the community specifically in Healthcare and Education. She is the former Chief Officer of the Lucid.ai Ethics Advisory Panel. Kay is an Associate Fellow of the Leverhulme Centre for the Future of Intelligence at the University of Cambridge and a Senior Fellow and Distinguished Scholar at the Robert S. Strauss Center for International Security and Law, University of Texas, Austin. She is Vice-Chair, The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems Additionally, she is a Partner in the Cognitive Finance group and an adjunct Professor of Law.
Kay is a humanitarian with a strong sense of social justice and has advanced degrees in Law and International Relations. She advises governments, think tanks, businesses, inter-governmental bodies and non-profits about artificial intelligence, law and policy. Kay co-founded the Consortium for Law and Policy of Artificial Intelligence and Robotics at the University of Texas and taught its first course: Artificial Intelligence and Emerging Technologies: Law and Policy.


BioData West: Day 1 @ 18:00

AI - The Ethical Debate

  • With unprecedented data sharing via the cloud we have been provided with vast data lakes at our fingertips. 
  • Harnessing meaning from this data is possible through AI. 
  • However, as AI advances we are unable to always disseminate why or how it has deduced meaning from the data.
  • With garbage in garbage out does AI become biased?

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