Paradoxes in Fair Machine Learning
Authors: Paul Goelz, Anson Kahng, Ariel D. Procaccia
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 {pgoelz, akahng, arielpro}@cs.cmu.edu |
| 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. |