Making Decisions that Reduce Discriminatory Impacts
Authors: Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach with an example: how to increase students taking college entrance exams in New York City public schools. |
| Researcher Affiliation | Academia | 1The Alan Turing Institute 2University of Oxford 3University of Surrey 4New York University 5University College London. |
| Pseudocode | No | The paper describes an optimization framework (MILP) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | We use the Python interface to the Gurobi optimization package to solve the MILP8. 8https://github.com/mkusner/reducing_discriminatory_impact |
| Open Datasets | Yes | We compiled a dataset on 345 high schools from the New York City Public School District, largely from the Civil Rights Data Collection (CRDC)6. 6https://ocrdata.ed.gov/ ... We construct both N(i) and s(i, j) using GIS coordinates for each school in our dataset7: 7https://data.cityofnewyork.us/Education/School-Point-Locations/jfju-ynrr |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It mentions fitting parameters via maximum likelihood using an 'observed dataset'. |
| Hardware Specification | No | The paper does not specify any hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions using 'the Python interface to the Gurobi optimization package' but does not specify version numbers for either Python or Gurobi. |
| Experiment Setup | Yes | We then solve the optimization problem in eq. (5) (using the MILP framework in Section 3.3) with the structural equation for Y in eq. (7), and a budget b of 25 schools. We construct both N(i) and s(i, j) using GIS coordinates for each school in our dataset7: N(i) is the nearest K = 5 schools to school i and s(i, j) is the inverse distance in GIS coordinate space. |