Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- fairness
+- classification
+metrics:
+- accuracy
+---
+
+# 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)**  
+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