SERC Symposium: Speaker Bio and Abstract
Maximilian Kasy, Professor of Economics, Oxford
In my talk, I will make the following arguments:
1. AI concerns the construction of systems which maximize a measurable objective (reward). Such systems take data as an input, and produce chosen actions as an output.
2. Maximization of a singular objective by autonomous systems is taking place in a social world where different individuals have divergent objectives. These divergent objectives might stand in conflict. Evaluated in terms of these divergent objectives, the actions and policies chosen by AI systems (almost) always generate winners and losers.
3. Going from individual-level assessments of gains and losses to society-level assessments requires aggregation, which trades off gains and losses across individuals. In order to normatively evaluate AI, as well as proposed regulations, we need to explicitly assess the resulting individual gains and losses, and explicitly aggregate these gains and losses across individuals.
4. The social issues raised by AI, including questions of fairness, privacy, value alignment, accountability, and automation, can only be resolved through democratic control of algorithm objectives, and of the means to obtain them – data and computational infrastructure. Democratic control requires public debate and binding collective decision-making, at many different levels of society.
Maximilian Kasy is Professor of Economics at the University of Oxford. He received his PhD at UC Berkeley and joined Oxford after appointments at UCLA and Harvard University. His current research interests focus on social foundations for statistics and machine learning, going beyond traditional single-agent decision theory. He also works on economic inequality, job guarantee programs, and basic income. He teaches a course on foundations of machine learning at the economics department at Oxford.