Bias in AI Gaming: Final Analysis

This repository contains a comprehensive analysis of algorithmic bias in AI systems applied to gaming environments. The analysis focuses on detecting, quantifying, and mitigating gender-related biases in prediction models.

Contents

• Final_Bias_in_AI_Gaming_Analysis.ipynb:
  A unified Jupyter Notebook combining all components of the project, including data preprocessing, bias analysis, model evaluation, and mitigation strategies.

Objectives

• Identify potential biases in AI-driven recommendation or prediction systems within gaming contexts.
• Evaluate fairness using statistical measures such as statistical parity and equalized opportunity.
• Implement mitigation techniques including data reweighting, resampling, and adversarial training.
• Analyze the trade-off between accuracy and fairness.

Methodology

The notebook is structured in the following sequence:

1. Data Preparation: Loading and preprocessing of input datasets.
2. Initial Modeling: Training baseline models to observe initial bias levels.
3. Bias Measurement: Quantitative analysis of model outputs by demographic groups.
4. Mitigation Techniques: Application of various fairness-enhancing strategies.
5. Post-Mitigation Evaluation: Assessing effectiveness of mitigation and summarizing outcomes.

Tools and Libraries

• Python (Pandas, NumPy, Scikit-learn)
• Visualization: Matplotlib, Seaborn
• Fairness Metrics: Custom implementations and external libraries as applicable

Results Summary

• Initial models demonstrated imbalanced predictions across gender groups.
• Post-mitigation models showed improved fairness with marginal trade-offs in predictive accuracy.
• Visualizations illustrate the shifts in outcome distributions and fairness metrics.

How to Use

To reproduce the analysis:

1. Clone this repository:

   git clone https://github.com/your_username/your_repository.git
   cd your_repository