Modeling the real world for safe and responsible AI systems

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My objective is to advance fundamental science and understanding of AI systems in ways relevant to their responsible deployment in the real world. Artificial intelligence (AI) systems are increasingly deployed in real-world settings, but we lack a rigorous science to understand or predict behavior in these settings. There is a 'specification gap' in the way we formalize problems for AI systems vs. practical reality and what we 'really want' from AI systems.  Even when we can formalize the problem addressed in quite clear statistical terms (e.g. supervised learning on a fixed dataset), there is much we still do not understand about how and why deep networks are able to generalize as well as they do, why they fail when they do, and how they will perform on out-of-distribution data. Progress on these questions is necessary for the responsible use of AI in socially, environmentally, and scientifically important applications. Industrial applications of AI are largely profit-motivated, and this influence permeates the field in which problem settings and methods are most studied. Substantial harm is caused to already-marginalized groups by unintended side effects of predictive algorithms built on biased data. And lack of understanding or guarantees about generalization, privacy, and robustness can present critical barriers in applying AI to address current climate, ecological, and epidemiological crises.  My research programme addresses these questions by modelling AI systems and `what goes into' them - not only data, but the broader learning environment including task design/specification, loss function, and regularization, as well as the broader societal context of deployment, including privacy considerations, trends and incentives, norms, and human biases. In the relatively short (5yr) term, planned outcomes of my technical research are (1) theoretical & experimental results which help better understand learning and generalization behaviour in more realistic settings, particularly for underspecified problems and out-of-distribution data, (1) a `toolbox' of practical methods for safe and responsible development of AI (e.g. safe and robust learning algorithms, metrics for representativeness and alignment, sandboxes for testing AI systems prior to deployment, unit tests for undesirable behaviours), (3) popularization of novel problem settings, with baseline results, for AI systems addressing high-impact social and environmental problems (e.g. estimating individual-level risk and impact from negative externalities such as climate change, pollution, or epidemic disease; polarization and distortion in content recommendation). Canada is already a leader in academic training of AI researchers. I believe we are well-positioned globally to lead and influence the development of this cross-cutting technology in a more safe and responsible direction. My research programme seeks to place this effort on a strong and actionable scientific foundation.