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PhD student in Explainable AI for Black Box Autonomous Agents

My research explores how we might begin to understand and explain the policies of black box autonomous agents, whose internal mechanisms and representations may be very different from our own, with a particular view to revealing the biases and flaws in their decision-making. Critical to this question are the putative trade-off between comprehensibility and performance of machine learning models, and the thorny relationship between correlation and causation in observed data whose generative origins are unknown.

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