FaireduPlus

Enhancing Intersectional Fairness in Education-Focused Machine Learning Using Synthetic Data

Requirements

• Python 3.9+
• Packages: pandas, numpy, scikit-learn, aif360, ctgan, sdgx, statsmodels
• For LLM generation set OPENAI_API_KEY (used by generate_sdg.generate_by_llm).

Data layout

• dataset_folder: https://zenodo.org/records/17933909. Test data is expected at <dataset_folder>/<dataset_name>/test_<dataset_name>.csv.
• Generated synthetic files are merged into the file name passed via --merged-output-file-name.

Quick start

python fairedu_plus.py \
  --dataset-name student_dropout \
  --dataset-folder /home/ad/m4do/proj/fairedu_plus/original_dataset \
  --generator LLM \
  --merged-output-file-name merged_output.csv \
  --seed 42

CLI options (fairedu_plus.py)

• --dataset-name (student_dropout, student_oulad, student_performance, DNU)
• --dataset-folder path to dataset root used to locate the test CSV
• --generator choose LLM or CTGAN
• --merged-output-file-name name for merged synthetic output
• --run-splitted-file / --no-run-splitted-file choose split vs combined training files
• --seed random seed for reproducibility

Additional examples

• CTGAN without split files:

python fairedu_plus.py --dataset-name student_dropout --generator CTGAN --no-run-splitted-file

• Using OULAD dataset with explicit dataset folder:

python fairedu_plus.py --dataset-name student_oulad --dataset-folder ./dataset

Citation

If you use this work, please cite the SSRN preprint:

SSRN Scholarly Paper 5290738. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5290738

Acknowledgements

This work builds upon the following open-source projects:

• https://github.com/hitsz-ids/synthetic-data-generator
• https://github.com/fairnesstest/LTDD