Thomas Krendl Gilbert received an interdisciplinary PhD in Machine Ethics and Epistemology at UC Berkeley. With prior training in philosophy, sociology, and political theory, Thomas designed his degree program to investigate the ethical and political predicaments that emerge when artificial intelligence reshapes the context of organizational decision-making. His recent work investigates how specific algorithmic learning procedures (such as reinforcement learning) reframe classical ethical questions and recall the foundations of democratic political philosophy, namely the significance of popular sovereignty and dissent for resolving normative uncertainty and modeling human preferences. This work has concrete implications for the design of AI systems that are fair for distinct subpopulations, safe when enmeshed with institutional practices, and accountable to public concerns, including medium-term applications like automated vehicles.