Fair Representations by Compression
Authors: Xavier Gitiaux, Huzefa Rangwala11506-11515
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that the proposed method, FBC, achieves state-of-the-art accuracyfairness trade-off. The objective of this experimental section is to demonstrate that Fairness by Binary Compression FBC can achieve state-of-the art performance compared to four benchmarks in fair representations learning: β-VAE, Adv, MMD and VFAE. |
| Researcher Affiliation | Academia | Xavier Gitiaux, Huzefa Rangwala George Mason University xgitiaux@gmu.edu, rangwala@gmu.edu |
| Pseudocode | No | The paper describes the method's steps in paragraph form, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology (FBC) is publicly available. |
| Open Datasets | Yes | First, we apply our experimental protocol to a synthetic dataset DSprites Unfair, 2 that contains 64 by 64 black and white images of various shapes (heart, square, circle). (Footnote 2: https://github.com/deepmind/dsprites-dataset/); Then, we extend our experimental protocol to three benchmark datasets in fair machine learning: Adults, Compas and Heritage. The Adults dataset 3 contains 49K individuals... (Footnote 3: https://archive.ics.uci.edu/ml/datasets/adult); The Compas data 4 contains 7K individuals... (Footnote 4: https://github.com/propublica/compas-analysis/); The Health Heritage dataset 5 contains 220K individuals... (Footnote 5: https://foreverdata.org/1015/index.html) |
| Dataset Splits | No | Datasets are split into a training set used to trained the encoderdecoder architecture; two test sets, one to train both task and auditing networks on samples not seen by the encoderdecoder; one to evaluate their respective performances. The paper does not explicitly mention a 'validation' dataset split or the proportions of the splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Pixel CNN' but does not provide specific version numbers for these or any other software dependencies, which are necessary for reproducibility. |
| Experiment Setup | No | The paper mentions hyperparameters like 'σ' for soft-binarization and 'β' for rate-distortion trade-off, and states that 'we vary the value of the parameter β', but it does not provide specific numerical values for these or other critical experimental setup details such as learning rate, batch size, or optimizer settings. |