Equality of Opportunity in Supervised Learning
Authors: Moritz Hardt, Eric Price, Eric Price, Nati Srebro
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the effect of non-discrimination on utility we used a loss in which false positives (giving loans to people that default on any account) is 82/18 as expensive as false negatives (not giving a loan to people that don t default). Given the marginal distributions for each group (Figure 3), we can then study the optimal profit-maximizing classifier under five different constraints on allowed predictors: Max profit has no fairness constraints, and will pick for each group the threshold that maximizes profit. This is the score at which 82% of people in that group do not default. Race blind requires the threshold to be the same for each group. Hence it will pick the single threshold at which 82% of people do not default overall. Demographic parity picks for each group a threshold such that the fraction of group members that qualify for loans is the same. Equal opportunity picks for each group a threshold such that the fraction of non-defaulting group members that qualify for loans is the same. Equalized odds requires both the fraction of non-defaulters that qualify for loans and the fraction of defaulters that qualify for loans to be constant across groups. This might require randomizing between two thresholds for each group. |
| Researcher Affiliation | Collaboration | Moritz Hardt Google m@mrtz.org Eric Price UT Austin ecprice@cs.utexas.edu Nathan Srebro TTI-Chicago nati@ttic.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | FICO scores are a proprietary classifier widely used in the United States to predict credit worthiness [11]. These scores, ranging from 300 to 850, try to predict credit risk; they form our score R. People were labeled as in default if they failed to pay a debt for at least 90 days on at least one account in the ensuing 18-24 month period; this gives an outcome Y . Our protected attribute A is race, which is restricted to four values: Asian, white non-Hispanic (labeled white in figures), Hispanic, and black. |
| Dataset Splits | No | The paper mentions 'labeled training data' and that data is 'used to construct a (possibly randomized) predictor' and 'to test for non-discriminatory', but does not specify any training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or version numbers. |
| Experiment Setup | No | The paper discusses theoretical concepts and a case study using FICO scores but does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings for model training. |