Conditional Learning of Fair Representations
Authors: Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations for classification. and 4 EMPIRICAL STUDIES In light of our theoretic findings, in this section we verify the effectiveness of the proposed algorithm in simultaneously ensuring equalized odds and accuracy parity using real-world datasets. |
| Researcher Affiliation | Collaboration | Han Zhao & Amanda Coston Machine Learning Department Carnegie Mellon University han.zhao@cs.cmu.edu acoston@andrew.cmu.edu Tameem Adel Department of Engineering University of Cambridge tah47@cam.ac.uk Geoffrey J. Gordon Microsoft Research, Montreal Machine Learning Department Carnegie Mellon University geoff.gordon@microsoft.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | To this end, we perform experiments on two popular real-world datasets in the literature of algorithmic fairness, including an income-prediction dataset, known as the Adult dataset, from the UCI Machine Learning Repository (Dua & Graff, 2017), and the Propublica COMPAS dataset (Dieterich et al., 2016). |
| Dataset Splits | Yes | Train / Test D0(Y = 1) D1(Y = 1) BR(D0, D1) D(Y = 1) D(A = 1) Adult 30, 162/15, 060 0.310 0.113 0.196 0.246 0.673 COMPAS 4, 320/1, 852 0.400 0.529 0.129 0.467 0.514 |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The hyperparameters used in the experiment are listed in Table 2. Optimization Algorithm Ada Delta Learning Rate 1.0 Batch Size 512 Training Epochs λ {0.1, 1.0, 10.0, 100.0, 1000.0} 100 and The hyperparameters used in the experiment are listed in Table 3. Optimization Algorithm Ada Delta Learning Rate 1.0 Batch Size 512 Training Epochs λ {0.1, 1.0} 20 Training Epochs λ = 10.0 15 |