In this talk Professor Kasy will draw about a chapter in his paper that discusses the regulation of artificial intelligence (AI) from the vantage point of political economy. By “political economy” he means a perspective which emphasises that there are different people and actors in society who have divergent interests and unequal access to resources and power. By “artificial intelligence” he means the construction of autonomous systems that maximise some notion of reward. The construction of such systems typically draws on the tools of machine learning and optimisation.
AI and machine learning are used in an ever wider array of socially consequential settings. This includes labour markets, education, criminal justice, health, banking, housing, as well as the curation of information by search engines, social networks, and recommender systems. There is a need for public debates about desirable directions of technical innovation, the use of technologies, and constraints to be imposed on technologies. In this talk he will review some frameworks to help structure such debates.
My discussion draws on concepts and references from machine learning, economics, and social choice theory. He will touch on several debates regarding the ethics and social impact of artificial intelligence, without any pretension of doing justice to the vast and growing literature on these topics; instead his goal is to give an internally coherent and principled account.
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If attending in person we look forward to seeing you in Manor Road Building Room G (Manor Road, Oxford, OX1 3UQ). If you are feeling unwell please do not attend and instead watch the event virtually.
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Please note that, with the speakers permission, we will be recording this event for those who are unable to attend. The recording will be added to the event entry on the website under 'Past Events'.
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Professor Maximilian Kasy
Professor of Economics, University of Oxford
Professor Maximilian Kasy is a Professor of Economics at the University of Oxford.His research interests include:
- Machine learning theory; the social impact of algorithmic decision making
- Publication bias, pre-analysis plans, and statistics as a social process
- Adaptive experimental design
- Statistical decision theory
- Empirical Bayes methods
- Identification and causality
- Economic inequality and (optimal) taxation.
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