Statistical Collusion by Collectives on Learning Platforms

Etienne Gauthier, Francis Bach, Michael I. Jordan
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:18897-18919, 2025.

Abstract

As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-gauthier25a, title = {Statistical Collusion by Collectives on Learning Platforms}, author = {Gauthier, Etienne and Bach, Francis and Jordan, Michael I.}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {18897--18919}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/gauthier25a/gauthier25a.pdf}, url = {https://proceedings.mlr.press/v267/gauthier25a.html}, abstract = {As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.} }
Endnote
%0 Conference Paper %T Statistical Collusion by Collectives on Learning Platforms %A Etienne Gauthier %A Francis Bach %A Michael I. Jordan %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-gauthier25a %I PMLR %P 18897--18919 %U https://proceedings.mlr.press/v267/gauthier25a.html %V 267 %X As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.
APA
Gauthier, E., Bach, F. & Jordan, M.I.. (2025). Statistical Collusion by Collectives on Learning Platforms. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:18897-18919 Available from https://proceedings.mlr.press/v267/gauthier25a.html.

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