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..
Paradoxes in Fair Machine Learning
Authors: Paul Goelz, Anson Kahng, Ariel D. Procaccia
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate the cost of allocation rules that satisfy both equalized odds and axioms of fair division on a dataset of FICO credit scores. We evaluate our approach on a dataset relating the FICO credit scores of 174 047 individuals to credit delinquency. |
| Researcher Affiliation | Academia | Paul Gölz, Anson Kahng, and Ariel D. Procaccia Computer Science Department Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and refers to an algorithm in the appendix, but no structured pseudocode or algorithm blocks are present in the main body of the paper. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/pgoelz/equalized. |
| Open Datasets | Yes | We evaluate our approach on a dataset relating the FICO credit scores of 174 047 individuals to credit delinquency. The dataset is based on Trans Union s Trans Risk scores, and was originally published by the Federal Reserve [2]. We use a cleaned and aggregated version made publicly available by Barocas et al. [3] at https://github.com/fairmlbook/fairmlbook.github.io/tree/master/code/ creditscore. |
| Dataset Splits | No | The paper describes how individuals are partitioned into buckets based on credit scores and race but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper provides a link to its public code repository, but it does not explicitly list specific software dependencies with version numbers within the main text. |
| Experiment Setup | No | The paper describes the parameters of the allocation problem (like 'k' for number of loans) but does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings typically found in machine learning experiments. |