Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Identifying Statistical Bias in Dataset Replication
Authors: Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study Image Net-v2, a replication of the Image Net 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% 1.5% of the original 11.7% 1.0% accuracy drop remains unaccounted for. |
| Researcher Affiliation | Academia | 1MIT 2UC Berkeley. Correspondence to: Logan Engstrom <EMAIL>. |
| Pseudocode | Yes | We provide further detail (including pseudocode) on the fitting process for pi(s(x); θ) in Appendix F. |
| Open Source Code | Yes | Code for our study is publicly available1. 1https://git.io/data-rep-analysis |
| Open Datasets | Yes | Image Net (Deng et al., 2009; Russakovsky et al., 2015) (which we also refer to as Image Net-v1 or v1) is one of the most widely used datasets in computer vision. |
| Dataset Splits | No | The paper refers to pre-existing datasets like ImageNet and ImageNet-v2, and their respective 'test sets', but does not explicitly provide details about training, validation, and test splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper refers to using 'Amazon Mechanical Turk (MTurk)' and mentions 'PyTorch' in a reference's title, but does not provide specific version numbers for any software dependencies used in their experiments. |
| Experiment Setup | Yes | In these tasks, MTurk annotators were shown grids of 48 images at a time, each corresponding to an Image Net class. ... Each image was seen by 40 distinct annotators... We opt to use mixtures of beta distributions as the family pi( ; θ) ... a cubic spline |