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..
The Implicit Fairness Criterion of Unconstrained Learning
Authors: Lydia T. Liu, Max Simchowitz, Moritz Hardt
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify our theoretical findings with experiments on two well-known datasets, demonstrating the effectiveness of unconstrained learning in achieving approximate calibration with respect to multiple group attributes simultaneously. (Section 1) |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | These are the Adult dataset from the UCI Machine Learning Repository (Dua and Karra Taniskidou, 2017) and a dataset of pretrial defendants from Broward County, Florida (Angwin et al., 2016; Dressel and Farid, 2018) (Section 3). |
| Dataset Splits | Yes | Score functions are obtained by logistic regression on a training set that is 80% of the original dataset, using all available features, unless otherwise stated. (Section 3) |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions that score functions are obtained by 'logistic regression' but does not specify any software names with version numbers (e.g., Python, PyTorch, scikit-learn, etc.). |
| Experiment Setup | Yes | Score functions are obtained by logistic regression on a training set that is 80% of the original dataset, using all available features, unless otherwise stated. (Section 3). In Figure 6 (top), we implicitly restrict the model class by varying the regularization parameter: with a smaller parameters corresponding to more severe regularization, constraining the learned weights to be inside a smaller L1 ball. (Section 3.3) |