The theory of algorithmic fairness has given rise to new fundamental questions and new insights into old questions. This talk outlines one such question — what is the meaning of an “individual probability”? — situating the problem in the context of algorithmic fairness. We propose a notion, Outcome Indistinguishability, and briefly illustrate the breadth of applications of the powerful underlying paradigm.
Introduction
This conference – organized under the auspices of the Isaac Newton Institute “Mathematics of Deep Learning” Programme — brings together leading researchers along with other stakeholders in industry and society to discuss issues surrounding trustworthy artificial intelligence.
This conference will overview the state-of-the-art within the wide area of trustworthy artificial intelligence including machine learning accountability, fairness, privacy, and safety; it will overview emerging directions in trustworthy artificial intelligence, and engage with academia, industry, policy makers, and the wider public.
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