---
license: mit
tags:
- fairness
- classification
metrics:
- accuracy
papers:
  - https://arxiv.org/abs/2507.20708
---

# EIF Biased Classifiers (Multi-Dataset Benchmark)

## 📌 Overview

This repository contains a collection of neural network models trained on seven tabular datasets for the study:

**Exposing the Illusion of Fairness (EIF): Auditing Vulnerabilities to Distributional Manipulation Attacks**  
https://arxiv.org/abs/2507.20708  

Codebase:  
https://github.com/ValentinLafargue/Inspection  

Each model corresponds to a specific dataset and is designed to analyze fairness properties rather than maximize predictive performance.

## 🧠 Model Description

All models are **multilayer perceptrons (MLPs)** trained on tabular data.

- Fully connected neural networks  
- Hidden layers: configurable (`n_loop`, `n_nodes`)  
- Activation: ReLU (optional)  
- Output: Sigmoid  
- Prediction: $\hat{Y} \in [0,1]$  

## 📊 Datasets, Sensitive Attributes, and Disparate Impact

| Dataset | Adult[1] | INC[2] | TRA[2] | MOB[2] | BAF[3] | EMP[2] | PUC[2] |
|--------|------|-----|-----|-----|-----|-----|-----|
| **Sensitive Attribute (S)** | Sex | Sex | Sex | Age | Age | Disability | Disability |
| **Disparate Impact (DI)** | 0.30 | 0.67 | 0.69 | 0.45 | 0.35 | 0.30 | 0.32 |
```
[1]: Becker, B. and Kohavi, R. (1996). Adult. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5XW20.306,
https://www.kaggle.com/datasets/uciml/adult-census-income.

[2]: Ding, F., Hardt, M., Miller, J., and Schmidt, L. (2021). Retiring adult: New datasets for fair machine learning. In Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems.313,
https://github.com/socialfoundations/folktables.

[3]: Jesus, S., Pombal, J., Alves, D., Cruz, A., Saleiro, P., Ribeiro, R. P., Gama, J., and Bizarro, P. (2022). Turning the tables: Biased, imbalanced, dynamic tabular datasets for ml evaluation. In Advances in Neural Information Processing Systems,
https://www.kaggle.com/datasets/sgpjesus/bank-account-fraud-dataset-neurips-2022.
```

### Notes
- Adult dataset: 5,000 test samples  
- Other datasets: 20,000 test samples  
- Sensitive attributes are used for fairness evaluation  

## 📈 Predictive Performance (Accuracy)

| Dataset | Accuracy |
|--------|----------|
| Adult Census Income | 84% |
| Folktables Income (INC) | 88% |
| Folktables Mobility (MOB) | 84% |
| Folktables Employment (EMP) | 77% |
| Folktables Travel Time (TRA) | 72% |
| Folktables Public Coverage (PUC) | 73% |
| Bank Account Fraud (BAF) | 98% |

**Note:** High performance on BAF is due to strong class imbalance.  
Accuracy was **not the main objective** of this study.

## 🎯 Intended Use

These models are intended for:

- Fairness analysis  
- Studying disparate impact and bias  
- Reproducing results from the EIF paper  
- Benchmarking fairness-aware methods  

## ⚠️ Limitations and Non-Intended Use

- Not designed for production  
- Not optimized for predictive performance  
- Should not be used for real-world decision-making  

These models intentionally expose biases in standard ML pipelines.

## ⚖️ Ethical Considerations

This work highlights:
- The presence of bias in machine learning models  
- The limitations of fairness metrics  

Models should be interpreted as **analytical tools**, not fair systems.

## 📦 Repository Structure

Each dataset corresponds to a subfolder:

EIF-biased-classifier/ <br/>
 ├── ASC_ADULT_model/<br/>
 ├── ASC_INC_model/<br/>
 ├── ASC_MOB_model/<br/>
 ├── ASC_EMP_model/<br/>
 ├── ASC_TRA_model/<br/>
 ├── ASC_PUC_model/<br/>
 └── ASC_BAF_model/<br/>

Each folder contains:
- `config.json`
- `model.safetensors`

## 🚀 Usage

```python
model = Network.from_pretrained(
    "ValentinLAFARGUE/EIF-biased-classifier",
    subfolder="ASC_INC_model"
)
```

## 📚 Citation

```
@misc{lafargue2026exposingillusionfairnessauditing,
      title={Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks}, 
      author={Valentin Lafargue and Adriana Laurindo Monteiro and Emmanuelle Claeys and Laurent Risser and Jean-Michel Loubes},
      year={2026},
      eprint={2507.20708},
      url={https://arxiv.org/abs/2507.20708}, 
}
```

## 🔍 Additional Notes

- Models are intentionally simple to isolate fairness behavior
- Results depend on preprocessing and sampling choices
- Focus is on reproducibility