Algorithmic Collective Action in Machine Learning | Social Foundations of Computation – Max Planck Institute for Intelligent Systems

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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

Algorithmic Collective Action in Machine Learning

Aca — The collective plants a hidden rare signal in the data and exploits the signal at test time. Success grows rapidly as a function of the fraction of workers participating. Experiments with language models on freelancer resumes confirm our theoretical prediction.

We propose a theoretical model of algorithmic collective action. Participating workers modify their data in a coordinated manner in order to steer the learning algorithm toward a desired outcome. Even small collectives can have significant influence [].

Members

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Algorithms and Society

Celestine Mendler-Dünner

Hector Endowed Fellow of the ELLIS Institute

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Social Foundations of Computation

Moritz Hardt

Director

Publications

Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR, The Forty International Conference on Machine Learning (ICML), July 2023 (Published) URL BibTeX