The Invisible Hand of Prediction

  • Speaker
  • Moritz Hardt, Ph.D.Director Social Foundations of Computation, Max Planck Institute
Date & Time

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Algorithmic predictions steer markets, drive consumption, shape communities and alter life trajectories. However, the theory and practice of machine learning have long neglected the often-invisible causal forces of prediction. A recent conceptual framework, called performative prediction, draws attention to the fundamental difference between learning from a population and steering a population through predictions.

In this lecture, after covering some emerging insights on performative prediction, Moritz Hardt will turn to an application of performativity to the question of power in digital economies. Traditional economic concepts struggle with identifying anti-competitive patterns in digital platforms, not least due to the difficulty of defining the market. Next, he will introduce the notion of performative power that sidesteps the complexity of market definition and directly measures how much a firm can benefit from steering consumer behavior. Finally, he will discuss the normative implications of high performative power, its connections to measures of market power in economics, and its relationship to ongoing antitrust debates.

To attend this in-person event, you will need to register in advance and provide:

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On-site registration will not be permitted. Walk-in entry will be denied.

About the Speaker

Hardt is a director at the Max Planck Institute for Intelligent Systems. Before joining the institute, he was an associate professor for electrical engineering and computer sciences at the University of California, Berkeley. His research contributes to the scientific foundations of machine learning and algorithmic decision-making from a social perspective. He is known for his work on fairness, privacy, scientific validity and strategic behavior in algorithmic systems. Hardt co-founded the Workshop on Fairness, Accountability, and Transparency in Machine Learning. He is a co-author of the textbooks “Fairness and Machine Learning: Limitations and Opportunities” (MIT Press) and “Patterns, Predictions, and Actions: Foundations of Machine Learning” (Princeton University Press).

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