We’re delighted to invite you to a seminar by Prof Shannon Vallor. Part of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI) seminar series.
Abstract: In a world where AI seems to be taking over more and more human cognitive labour, while placing ever greater demands on our environment, this talk asks a crucial question: can we live with AI? More importantly, what would it mean to live with it well? This talk aims to provide a nuanced critical understanding of AI’s strengths and limitations; of the values and knowledge embedded in it, and the nature of human responsibility for decisions made with it.
Speaker Bio: Prof. Shannon Vallor is the Baillie Gifford Chair in the Ethics of Data and Artificial Intelligence at the Edinburgh Futures Institute (EFI) at the University of Edinburgh, where she is also appointed in Philosophy. She is Director of the Centre for Technomoral Futures in EFI, and co-Director of the BRAID (Bridging Responsible AI Divides) programme, funded by the Arts and Humanities Research Council. Professor Vallor’s research explores how new technologies, especially AI, robotics, and data science, reshape human moral character, habits, and practices. Her work includes advising policymakers and industry on the ethical design and use of AI.
She is a standing member of the One Hundred Year Study of Artificial Intelligence (AI100) and a member of the Oversight Board of the Ada Lovelace Institute. Professor Vallor received the 2015 World Technology Award in Ethics from the World Technology Network and the 2022 Covey Award from the International Association of Computing and Philosophy. She is a former Visiting Researcher and AI Ethicist at Google. In addition to her many articles and published educational modules on the ethics of data, robotics, and artificial intelligence, she is the author of the book ‘Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting’ (Oxford University Press, 2016) and ‘The AI Mirror: Reclaiming Our Humanity in an Age of Machine Thinking‘ (Oxford University Press, 2024).