The question we'll explore in this talk is how we can redirect the path we are taking as AI designers towards the promotion of shared prosperity, and in designing and deploying AI in ways where both the benefits, and the risks, of new technology are shared equally across society. I'll explore these ideas by combining different elements of my recent work. I'll start with a concrete problem of developing AI for detecting organ damage in hospitals. This use-case will highlight the interconnected sociotechnical system that machine learning operates within. This system has sets of values and politics that must contend with a colonial legacy and coloniality, and I'll explore thinking on decolonial AI, and also delve into a further use case on queer fairness. Along the way, I'll try to connect to the work of other organistions, like the AI and Shared Prosperity initiative by the Partnership on AI, and Royal Society programme on digital technologies for the planet.

Bio: Shakir Mohamed is a research scientist and lead at DeepMind in the areas of statistical machine learning and artificial intelligence. He works on advancing machine learning principles, applied questions in healthcare and climate, and sociotechnical systems and transformation. Shakir is also a founder and trustee of the Deep Learning Indaba, a grassroots organisation aiming to build pan-African capacity and ownership in AI. Shakir is the General Chair for the 2021 International conference on Learning Representations, a member of the Royal Society's Diversity Committee, an Associate Fellow of the Leverhulme Centre for the Future of Intelligence, and an Honorary Professor of University College London