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Poster
Identifying Statistical Bias in Dataset Replication
Logan Engstrom · Andrew Ilyas · Shibani Santurkar · Dimitris Tsipras · Jacob Steinhardt · Aleksander Madry

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @ None #None
in Poster Session 21 »

Dataset replication is a useful tool for assessing whether improvements in test accuracy on a specific benchmark correspond to improvements in models' ability to generalize reliably. In this work, we present unintuitive yet significant ways in which standard approaches to dataset replication introduce statistical bias, skewing the resulting observations. We study ImageNet-v2, a replication of the ImageNet dataset on which models exhibit a significant (11-14%) drop in accuracy, even after controlling for selection frequency, a human-in-the-loop measure of data quality. We show that after remeasuring selection frequencies and correcting for statistical bias, only an estimated 3.6% of the original 11.7% accuracy drop remains unaccounted for. We conclude with concrete recommendations for recognizing and avoiding bias in dataset replication. Code for our study is publicly available: https://git.io/data-rep-analysis.

Keywords: Robust Statistics and Machine Learning · Trustworthy Machine Learning · Other

Author Information

Logan Engstrom (MIT)
Andrew Ilyas (Massachusetts Institute of Technology)
Shibani Santurkar (MIT)
Dimitris Tsipras (MIT)
Jacob Steinhardt (University of California, Berkeley)
Aleksander Madry (MIT)

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