Dont Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget | Social Foundations of Computation – Max Planck Institute for Intelligent Systems

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Performative Prediction: Past and Future

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Digital Platforms, Power and Work

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Fairness and Machine Learning: Limitations and Opportunities

Unprocessing Seven Years of Algorithmic Fairness

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Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget

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Training on the Test Task Confounds Evaluation and Emergence

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Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget

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It’s widely believed that clean labels are important for benchmarking purposes. Dataset creators therefore often take a majority vote over multiple labels per data point. Contrary to conventional wisdom, we prove that when labels are costly and may contain errors, picking one label per data point works best for comparing the accuracy of two models.

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Social Foundations of Computation Conference Paper Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget Dorner, F. E., Hardt, M. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR, The Forty-First International Conference on Machine Learning (ICML), July 2024 (Published) ArXiv URL BibTeX