Average Individual Fairness: Algorithms, Generalization and Experiments
Authors: Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we implement our algorithm and empirically verify its effectiveness. We have implemented the AIF-Learn algorithm and conclude with a brief experimental demonstration of its practical efficacy using the Communities and Crime dataset. |
| Researcher Affiliation | Academia | Michael Kearns University of Pennsylvania mkearns@cis.upenn.edu Aaron Roth University of Pennsylvania aaroth@cis.upenn.edu Saeed Sharifi-Malvajerdi University of Pennsylvania saeedsh@wharton.upenn.edu |
| Pseudocode | Yes | Subroutine 1: BEST best response of the Learner in the AIF setting. Algorithm 2: AIF-Learn learning subject to AIF. Mapping 3: b ψ (X, c W) pseudocode. |
| Open Source Code | No | The paper does not provide explicit statements about releasing source code for the described methodology or links to code repositories. |
| Open Datasets | Yes | We have implemented the AIF-Learn algorithm and conclude with a brief experimental demonstration of its practical efficacy using the Communities and Crime dataset. Described in detail and available for download at http://archive.ics.uci.edu/ml/datasets/ communities+and+crime |
| Dataset Splits | No | The paper mentions training and test sets but does not specify details for a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions using a "linear threshold learning heuristic" but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | To obtain a challenging instance of our multi-problem framework, we treated each of the first n = 200 neighborhoods as the individuals in our sample, and binarized versions of the first m = 50 variables as distinct prediction problems. Another d = 20 of the variables were used as features for learning. For the base learning oracle assumed by AIF-Learn, we used a linear threshold learning heuristic that has worked well in other oracle-efficient reductions (Kearns et al. (2018)). |