Performative Power | Social Foundations of Computation – Max Planck Institute for Intelligent Systems

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Research Overview

Social Prediction

Performative Prediction: Past and Future

Difficult Lessons on Social Prediction from Wisconsin Public Schools

Allocation Requires Prediction Only if Inequality Is Low

Digital Platforms, Power and Work

Performative Power

An Engine Not a Camera: Measuring Performative Power of Online Search

Causal Inference from Competing Treatments

Contesting Algorithmic Systems

Algorithmic Collective Action in Machine Learning

Decline Now: A Combinatorial Model for Algorithmic Collective Action

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists

Algorithmic Fairness

Fairness and Machine Learning: Limitations and Opportunities

Unprocessing Seven Years of Algorithmic Fairness

Science of Machine Learning Benchmarks

ImageNot: A Contrast with ImageNet Preserves Model Rankings

Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget

A Theory of Dynamic Benchmarks

Evaluating Large Language Models

Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks

Evaluating Language Models as Risk Scores

Fine-tuning Large Language Models

Training on the Test Task Confounds Evaluation and Emergence

Lawma: The Power of Specialization for Legal Tasks

Social Foundations of Computation Members Publications

Performative Power

Performative power is a new notion of power that quantifies how much a firm can steer a target population through its algorithmic actions. We show that the definition enjoys many desirable theoretical properties, while proving identifiability from observational causal inference [].

Members

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Social Foundations of Computation

Moritz Hardt

Director

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Algorithms and Society

Celestine Mendler-Dünner

Hector Endowed Fellow of the ELLIS Institute

Publications

Social Foundations of Computation Algorithms and Society Conference Paper Performative Power Hardt, M., Jagadeesan, M., Mendler-Dünner, C. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), Curran Associates Inc., The Thirty-Six Annual Conference on Neural Information Processing Systems (NeurIPS), March 2022 (Published) URL BibTeX