Deep learning has resulted in rapid progress in the field of machine learning and artificial intelligence, leading to dramatically improved solutions of many challenging problems. As these models move out from the research lab into real work applications, it is important to ensure that they are safe, reliable and trustworthy. In this talk, I will review some of the challenges that arise in this direction and introduce the framework of spec-consistent machine learning that can help ML researchers formalize notions of desired behavior of the AI system.
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.
Add comment