Fair Hiring Model: Bias Mitigation in AI

📌 Project Overview

This project aims to develop a plug-and-play fairness module that integrates into any AI workflow to continuously monitor and mitigate bias. It supports various data types (tabular, images, text) and operates in binary/multiclass classification and regression settings.

The key innovation is the use of adversarial training with a Gradient Reversal Layer (GRL) to remove biased information from AI decisions while maintaining predictive accuracy.

🎯 Objectives

• ✅ Ensure fairness by minimizing the influence of sensitive attributes (e.g., gender, race) on hiring decisions.
• ✅ Maintain model performance while debiasing the learned representations.
• ✅ Generalize to different datasets even if they have different numbers of features.

🚀 How It Works

The model consists of:

1. A Shared Feature Extractor → Learns representations of candidate profiles.
2. A Primary Predictor → Predicts hiring suitability based on extracted features.
3. An Adversarial Network → Tries to predict sensitive attributes (e.g., gender). The Gradient Reversal Layer (GRL) ensures that the shared representation removes bias by making the adversary’s task harder.

graph TD
    A[Input Data<br>Candidate Features] -->|Shared Representation| B(Feature Extractor)
    B -->|Fair Features| C[Hiring Decision Model]
    B -->|Reversed Gradient| D(Adversary<br>Sensitive Attribute Predictor)
    C -->|Predictions| E[Hiring Decision<br>Fair]
    D -->|Predicts Gender?| F[Bias Signal<br>for Backpropagation]
    
    classDef primary fill:#d0e0ff,stroke:#3080e0,stroke-width:2px
    classDef processor fill:#ffe0d0,stroke:#e08030,stroke-width:2px
    classDef output fill:#d0ffe0,stroke:#30e080,stroke-width:2px
    classDef feedback fill:#ffd0e0,stroke:#e03080,stroke-width:2px
    
    class A,F primary
    class B,D processor
    class C output
    class E output

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📊 Evaluating Fairness and Performance

We compute the following metrics:

1️⃣ Hiring Decision Performance

• Accuracy (Primary Task): Measures how well the model predicts hiring suitability.
• F1 Score (Primary Task): Evaluates the balance between precision and recall.

2️⃣ Bias Detection Performance

• Adversary Accuracy (Sensitive Attribute Prediction):
  • 🔼 High → The adversary can predict gender, meaning bias is present.
  • 🔽 Low → The adversary struggles, meaning the model is fair.
• Adversary F1 Score (Sensitive Attribute Prediction):
  • Measures how well gender is predicted.

🛠 Desired Trade-Off

Scenario	Interpretation
High primary accuracy + High adversary accuracy	🚨 Model is biased; hiring decision leaks sensitive information.
High primary accuracy + Low adversary accuracy	✅ Model is fair; hiring decision does not depend on sensitive attributes.
Low primary accuracy + Low adversary accuracy	🤔 Model might be underfitting or fairness regularization is too strong.
Low primary accuracy + High adversary accuracy	❌ Model is learning biases but failing at hiring decisions.

🛠 How to Use

1. Install dependencies:

   pip install torch numpy scikit-learn

2. Train the Model:

   model = FairHiringModel(input_dim=20, lambda_adv=1.0)
   optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

3. Adjust Fairness During Training:

   model.lambda_adv = 0.5  # Weaker debiasing mid-training

4. Monitor Performance & Fairness:

   primary_acc = accuracy_score(y_true, primary_preds_bin)
   sensitive_acc = accuracy_score(s_true, sensitive_preds_bin)
   print("Hiring Accuracy:", primary_acc, "Bias Detection Accuracy:", sensitive_acc)

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