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About this paper
David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni and 3 more
The authors argue that machine-learning research should begin with real-world problems, users, data, and success criteria, alongside methods-driven work. They propose collaborating with stakeholders to frame tasks, understand data constraints, and choose methods and metrics. Examples illustrate cross-field contributions, but this position paper presents no comparison or impact measurement, and not every application paper fits an ML venue.
Summary by alphaXiv.
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