Delayed Impact of Fair Machine Learning
Authors: Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on FICO credit score data from 2003 and show that under various models of bank utility and score change, the outcomes of applying fairness criteria are in line with our theoretical predictions. |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA. |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code for the described methodology. |
| Open Datasets | Yes | Our FICO data is based on a sample of 301,536 Trans Union Trans Risk scores from 2003 (US Federal Reserve, 2007) |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits, percentages, or methodology for data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We model individual penalties as a score drop of c = 150 in the case of a default, and in increase of c+ = 75 in the case of successful repayment. ... For a model with bank utilities set to (a) u u+ = 4 and (b) u u+ = 10. |