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..
Achieving Non-Discrimination in Prediction
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate the theoretical results and show the effectiveness of our two-phase framework. |
| Researcher Affiliation | Academia | Lu Zhang, Yongkai Wu, and Xintao Wu University of Arkansas EMAIL |
| Pseudocode | Yes | Algorithm 1: Two-phase framework. 1 If DED+εh,D τ, we are done. Otherwise, modify the labels in the training dataset D to obtain a modified dataset D such that |DED | τ. 2 Train a classifier h on D . If DED +εh ,D τ, we are done. Otherwise, tweak classifier h to meet the above requirement. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We first learn a causal model M for a real dataset, the Adult dataset [Lichman, 2013]... [Lichman, 2013] M. Lichman. UCI machine learning repository. http://archive.ics.uci.edu/ml, 2013. |
| Dataset Splits | No | The paper uses 'training data' but does not specify explicit train/validation/test splits, percentages, or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or processing units) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Tetrad' and 'decision tree (DT) and support vector machine (SVM)' but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | No | The paper mentions the discrimination threshold (τ = 0.05) and algorithms used, but does not specify concrete hyperparameters or detailed training configurations (e.g., learning rates, batch sizes, specific DT/SVM parameters) for the models. |