Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Understanding Instance-Level Impact of Fairness Constraints
Authors: Jialu Wang, Xin Eric Wang, Yang Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate with extensive experiments that training on a subset of weighty data examples leads to lower fairness violations with a trade-off of accuracy. (Abstract) and In this section, we examine the influence score subject to parity constraints on three different application domains: tabular data, images and natural language. (Section 6) |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, University of California, Santa Cruz, CA, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We publish the source code at https://github.com/UCSC-REAL/Fair Infl. |
| Open Datasets | Yes | Firstly, we work with multi-layer perceptron (MLP) trained on the Adult dataset (Dua & Graff, 2017)., Next, we train a Res Net-18 network (He et al., 2015) on the Celeb A face attribute dataset (Liu et al., 2015)., Lastly, we consider Jigsaw Comment Toxicity Classification (Jigsaw, 2018) with text data. |
| Dataset Splits | No | The paper describes training and test splits for the datasets but does not explicitly mention a validation dataset split ratio or strategy. |
| Hardware Specification | Yes | For all the experiments, we use a GPU cluster with four NVIDIA RTX A6000 GPUs for training and evaluation. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and pre-trained models (BERT) but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We used the Adam optimizer with a learning rate of 0.001 to train all the models. We used γ = 1 for models requiring the regularizer parameter of fairness constraints. (Section 6.1) and The MLP model is a two-layer Re LU network with hidden size 64. (Section 6.2) |