[Submitted on 31 Oct 2026]
[View PDF](https://arxiv.org/pdf/2611.01226)
[HTML (experimental)](https://arxiv.org/html/2611.01226v1)
Abstract:Machine learning models are increasingly used to not only inform, but make decisions. Sometimes platforms add value by pooling data across contexts to improve models' decision making---but when the optimal decision differs by context, pooling may not increase accuracy. We argue that a platform storing starkly distinct models across contexts nevertheless captures a model comparison externality. Together, these models reflect how decision makers respond differently to the same data in the current equilibrium. Policymakers can use this differentiation to prioritize interventions; firms can learn how they are already differentiated from competitors to focus future investments. Our evidence comes from an AI hiring platform used by large employers of low-skill workers. The platform uses a standardized psychometric assessment to create firm-specific models estimating each application's retention likelihood at that firm. Each firm's model is highly predictive for itself but no better than chance at other firms. We use these models to create a firm-application matrix: each firm scores each application, not only the ones it receives. An application's improved retention likelihood if submitted to a different firm is highly heterogeneous based on traits. Further, these gains are due to two distinct mechanisms. Finally, firms value traits differently even in this low skill market: they disagree about whether important traits improve or worsen retention at the margin. These insights are only possible when a platform can access both the full application pool and each firm-specific model. We explain the value of these insights to firms, policymakers, and platforms.
Current browse context:
econ.GN
References & Citations
...
Bibliographic Explorer
(What is the Explorer?) Connected Papers
(What is Connected Papers?) Litmaps
(What is Litmaps?) scite Smart Citations
(What are Smart Citations?) alphaXiv
(What is alphaXiv?) CatalyzeX Code Finder for Papers
(What is CatalyzeX?) DagsHub
(What is DagsHub?) Gotit.pub
(What is GotitPub?) Hugging Face
(What is Huggingface?) ScienceCast
(What is ScienceCast?) Influence Flower
(What are Influence Flowers?) CORE Recommender
(What is CORE?) arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.