Intersectional Unfairness Discovery
Authors: Gezheng Xu, Qi Chen, Charles Ling, Boyu Wang, Changjian Shui
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world text and image datasets demonstrate a diverse and efficient discovery of BGGN. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Western Ontario 2University of Toronto 3Vector Institute. Correspondence to: Boyu Wang <bwang@csd.uwo.ca>, Changjian Shui <changjian.shui@vectorinstitute.ai>. |
| Pseudocode | Yes | Algorithm 1 Bias Guided Generative Network (BGGN) |
| Open Source Code | Yes | The Code is available at: https://github.com/ xugezheng/BGGN. |
| Open Datasets | Yes | Celeb A (Image) (Liu et al., 2015) A face image dataset containing 200K images. ... Toxic (Text) (Borkan et al., 2019). The main task of this dataset is to predict the toxicity of text comments |
| Dataset Splits | Yes | We split the data into Observation (or training) and Holdout datasets, where there is no intersectional sensitive attribute overlap between these two sub-datasets. ... After obtaining this enriched dataset Dbias with bias value, we randomly split it into an Observation set (70%) and a Holdout set (30%) to train the bias value predictor \ Lf(a) and the generator, with NO sensitive attributes overlapping. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions 'Distil BERT' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We train f(x) for 3 epochs with a batch size of 64. We utilize the Adam optimizer and fix the learning rate at 1e-4 for both the backbone model and classifier. ... we train the predictor for 60 epoches using MSE loss and Adam optimizer with a learning rate of 1e-3. ... We first (pre-)train the vanilla generative model for 5 epoches with Adam optimizer and set the learning rate at 1e-3. ... We conducted 500 sampling iterations, with a batch size of 128 for each sampling. ... set a relatively small learning rate, with 2e-5 for the encoder and 1e-5 for the decoder. ... We set the resample number as 10, and the filter proportion as 0.2 on celeb A dataset. |