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..
FairSMOE: Mitigating Multi-Attribute Fairness Problem with Sparse Mixture-of-Experts
Authors: Changdi Yang, Zheng Zhan, Ci Zhang, Yifan Gong, Yize Li, Zichong Meng, Jun Liu, Xuan Shen, Hao Tang, Geng Yuan, Pu Zhao, Xue Lin, Yanzhi Wang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrated our effectiveness. Taking a Dei T-Small as the backbone, Fair SMo E achieves 77.25% and 86.01% accuracy on the ISIC2019 and Celeb A dataset respectively with Multi-attribute Predictive Quality Disparity (PQD) score of 0.801 and 0.787, beating current state-of-the-art methods such as Muffin and Multi Fair. |
| Researcher Affiliation | Collaboration | 1Northeastern University 2Microsoft Research 3University of Georgia 4Peking University |
| Pseudocode | Yes | Algorithm 1 Fairness-Guided Routing (FGR) |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our methods on the ISIC 2019 and Celeb A datasets, primarily for skin lesion analysis and facial attribute recognition tasks, respectively, with more details in Appendix. |
| Dataset Splits | Yes | We randomly separate ISIC2019 80:20 for training and test, and randomly select 5% of training set for validation. |
| Hardware Specification | Yes | 4 Nvidia A100s are used for training and testing. |
| Software Dependencies | No | The paper mentions 'Adam W' as an optimizer and 'Transformers' models but does not provide specific version numbers for software libraries, programming languages, or other dependencies. |
| Experiment Setup | Yes | We applied a batch size of 256 and data augmentation of Random Resized Crop for all methods on both datasets. Transformers are optimized with Adam W with weight decay of 1 10 4, initial learning rate (LR) of 5 10 4. Training epoch is set to 300 for ISIC2019 and 500 for Celeb A. We set ϖ as 0.6 in Equation (8) and ε as 0.1 in Ltotal. |