This repository contains the source code and data used for the following paper, appeared at ESEC/FSE 2021. The repository been also evaluated and acceped in the artifact track of the conference. For any questions, contact the corresponding author. The replication package is licensed under MIT License: Copyright (c) 2020 Sumon Biswas
Title Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline
Authors Sumon Biswas (sumon@iastate.edu) and Hridesh Rajan (hridesh@iastate.edu)
PDFhttps://arxiv.org/abs/2106.06054
- Source code
- Experiments
- Results (RQ1, RQ2, RQ3)
- DOI and Citation
The benchmark contains 37 ML pipelines under 5 different tasks from three prior studies.
| German Credit | Adult Census | Bank Marketing | Compas | Titanic |
|---|---|---|---|---|
| GC1 | AC1 | BM1 | CP1 | TT1 |
| GC2 | AC2 | BM2 | - | TT2 |
| GC3 | AC3 | BM3 | - | TT3 |
| GC4 | AC4 | BM4 | - | TT4 |
| GC5 | AC5 | BM5 | - | TT5 |
| GC6 | AC6 | BM6 | - | TT6 |
| GC7 | AC7 | BM7 | - | TT7 |
| GC8 | AC8 | BM8 | - | TT8 |
| GC9 | AC9 | - | - | - |
| GC10 | AC10 | - | - | - |
Cite the paper as:
@inproceedings{biswas21fair,
author = {Sumon Biswas and Hridesh Rajan},
title = {Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline},
booktitle = {ESEC/FSE'2021: The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
location = {Athens, Greece},
year = {2021},
entrysubtype = {conference},
url = {https://doi.org/10.1145/3468264.3468536},
}