A Mathematical Framework for AI-Human Integration in Work

L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:6978-7012, 2025.

Abstract

The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework’s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-celis25a, title = {A Mathematical Framework for {AI}-Human Integration in Work}, author = {Celis, L. Elisa and Huang, Lingxiao and Vishnoi, Nisheeth K.}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {6978--7012}, 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/celis25a/celis25a.pdf}, url = {https://proceedings.mlr.press/v267/celis25a.html}, abstract = {The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework’s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.} }
Endnote
%0 Conference Paper %T A Mathematical Framework for AI-Human Integration in Work %A L. Elisa Celis %A Lingxiao Huang %A Nisheeth K. Vishnoi %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-celis25a %I PMLR %P 6978--7012 %U https://proceedings.mlr.press/v267/celis25a.html %V 267 %X The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework’s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.
APA
Celis, L.E., Huang, L. & Vishnoi, N.K.. (2025). A Mathematical Framework for AI-Human Integration in Work. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:6978-7012 Available from https://proceedings.mlr.press/v267/celis25a.html.

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