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

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

Powermeter — PowerMeter is a browser extension that runs subtle randomized experiments on a participant’s search results in order to estimate the performative power of online search.

We published a first blueprint on how to estimate performative power in practice []. Our case study is the antitrust investigation by the European Commission against Google over its comparison shopping service.

Members

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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 An Engine Not a Camera: Measuring Performative Power of Online Search Mendler-Dünner, C., Carovano, G., Hardt, M. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2024 (Published) ArXiv URL BibTeX