Residual Unfairness in Fair Machine Learning from Prejudiced Data
Authors: Nathan Kallus, Angela Zhou
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate how systematic inclusion can affect fairness adjustments on an empirical example with data from the application of the Stop, Question, and Frisk (SQF) policy of the City of New York Police Department (NYPD). ... In Table 2, we report the estimated true positive and false positive rates achieved by these classifiers both in the training and in the target population. |
| Researcher Affiliation | Academia | 1Cornell University, and Cornell Tech, New York 2Cornell University, Ithaca, New York. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We consider learning a predictor of criminal possession of a weapon from this dataset... (Further details about SQF and the dataset are provided in the supplemental Sec. D.) ... We estimate the former from the SQF data and the latter from the 2010 American Community Survey data by matching census blocks to precincts and using Laplace smoothing. ... Keefe, J. Nypd stop-and-frisk data for you. Technical report, WNYC, 2011. ... NYCLU. Stop and frisk data, 2017. URL https://www.nyclu.org/en/stop-and-frisk-data. |
| Dataset Splits | No | The paper mentions training data and target population for evaluation but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for its experiments. |
| Software Dependencies | No | The paper mentions using logistic regression but does not specify any software components with version numbers (e.g., Python, library versions, or specific solvers). |
| Experiment Setup | No | The paper mentions fitting a logistic regression and setting an exchange rate of 25:1 for minimizing false negatives and false positives, but it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |