AI-Ethics-Auditor

AI Ethics Auditor

AI Ethics Auditor is an open-source toolkit designed to identify, explain, and mitigate bias in AI/ML models and datasets. It analyzes fairness, generates SHAP/LIME explanations for predictions, and suggests reweighting strategies for biased datasets. This MVP is a local web app, with plans to expand into plugins for Jupyter, VS Code, and ML platforms in the future.

🚀 Overview

• Bias Detection: Analyze datasets and models for fairness metrics like demographic parity.

• Explainability: Generate SHAP/LIME explanations for flagged biases.

• Mitigation: Apply reweighting or resampling to mitigate detected biases.

• Local & Modular: All processing is done locally, and the core functionality is designed for easy extension into popular ML environments.

🛠️ Key Features

• Fairness and bias analysis for models and datasets

• Interactive charts and explainability visualizations

• Reweighting strategies and fairness-aware algorithm recommendations

Ethical AI Principles & Predefined Criteria

We are using established AI ethics guidelines from:

• EU AI Act

• OECD AI Principles

• Fairness Indicators by Google

• IBM AI Fairness 360 Toolkit

📱 Usage

1. Analyze a Dataset:

   • Upload your dataset (CSV format) via the UI.

   • Select sensitive attributes (e.g., gender, race).

   • View fairness metrics and bias scores in visual charts.

2. Audit a Model:

   • Load a pretrained model (PyTorch or TensorFlow).

   • Run predictions on test data.

   • Generate SHAP explanations for any flagged biases.

3. Mitigate Bias:

   • Apply reweighting or resampling techniques to the biased data.

   • Download the debiased dataset or model.

   • Export a detailed PDF report for compliance purposes.