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The goal is to evaluate credit-risk models not only by predictive performance, but also by fairness, robustness, explainability, and governance compliance.\n\nThe system follows a sequential multi-agent workflow. Each agent produces reusable artifacts such as cleaned datasets, trained models, evaluation reports, rank-based metric outputs, compliance score tables, a System Card, and chatbot logs.\n\nThe project is evaluated on the German Credit dataset and is designed as a reproducible prototype for transparent and auditable AI governance in credit scoring.\n\n## Main Workflow\n\nThe pipeline is organized into five main stages:\n\n1. **Data Agent**  \n   Loads the German Credit dataset, performs exploratory data analysis, outlier analysis, preprocessing, train-test splitting, feature encoding, scaling, sensitive-feature extraction, and Data Card generation.\n\n2. **Modeling Agent**  \n   Trains candidate models including Logistic Regression, Random Forest, XGBoost, Voting Ensemble, Stacking Ensemble, and a Random Baseline.\n\n3. **Evaluation Agent**  \n   Computes predictive performance, fairness metrics, robustness metrics, rank-based SAFE AI metrics, SHAP--RGE explainability comparison, mitigation results, sensitivity analysis, and compliance score outputs.\n\n4. **Governance Agent**  \n   Computes the final SAFE governance score, compares it with the approval threshold, identifies the weakest SAFE dimension, and writes the System Card.\n\n5. **Chatbot Agent**  \n   Provides an artifact-grounded conversational interface for explaining the selected model, SAFE score, fairness results, mitigation effects, compliance score comparison, and governance decision.\n\n## SAFE Metrics\n\nThe project is inspired by the SAFE AI and Rank Graduation Box framework. It uses rank-based metrics including:\n\n- **RGA**: Rank Graduation Accuracy\n- **RGR**: Rank Graduation Robustness\n- **RGE**: Rank Graduation Explainability\n- **AURGA, AURGR, AURGE**: area-under-curve summaries of the corresponding rank-based curves\n\nThe final SAFE governance score combines:\n\n```text\nSAFE Score =\nW_RGA  × AURGA\n+ W_RGR  × AURGR\n+ W_RGE  × AURGE\n+ W_Fair × Fairness Aggregate\n```\n\nThe Compliance Score layer is separate from the final governance decision. It compares candidate models using AURGA, AURGR, and AURGE with aggregation methods such as arithmetic mean, geometric mean, RMS, and TOPSIS.\n\n## Project Structure\n\n```text\nSAFE-Agentic-Credit-System/\n├── data/\n│   ├── raw/\n│   ├── processed/\n│   └── sensitive/\n├── docs/\n│   ├── datacard.json\n│   ├── model_card.md\n│   └── system_card.md\n├── models/\n├── reports/\n│   ├── evaluation_report.md\n│   ├── final_report.md\n│   ├── sensitivity_report.md\n│   ├── mitigation_report.md\n│   └── figures/\n├── src/\n│   ├── chatbot.py\n│   ├── chat_cli.py\n│   ├── compliance.py\n│   ├── config.py\n│   ├── data_loader.py\n│   ├── evaluate.py\n│   ├── fairness.py\n│   ├── model.py\n│   ├── paths.py\n│   ├── preprocessing.py\n│   ├── reporting.py\n│   ├── rga.py\n│   ├── rge.py\n│   ├── rgr.py\n│   ├── shap_compare.py\n│   ├── train.py\n│   └── utils.py\n├── main.py\n└── README.md\n```\n\n## Installation\n\nClone the repository:\n\n```bash\ngit clone https://github.com/yasamin0/SAFE-Agentic-Credit-System.git\ncd SAFE-Agentic-Credit-System\n```\n\nCreate a virtual environment:\n\n```bash\npython -m venv .venv\n```\n\nActivate it:\n\n```bash\n# Windows\n.venv\\Scripts\\activate\n\n# macOS/Linux\nsource .venv/bin/activate\n```\n\nInstall the main dependencies manually:\n\n```bash\npip install pandas numpy scikit-learn matplotlib shap xgboost joblib python-dotenv crewai\n```\n\nDepending on your local environment, you may also need to install any additional packages imported by the project modules.\n\n## How to Run\n\nRun the full pipeline:\n\n```bash\npython main.py\n```\n\nRun the chatbot interface separately:\n\n```bash\npython src/chat_cli.py\n```\n\n## Generated Artifacts\n\nThe system generates:\n\n- cleaned train/test datasets\n- sensitive-feature files\n- trained model artifacts\n- Data Card, Model Card, and System Card\n- evaluation and final reports\n- fairness, robustness, calibration, and mitigation outputs\n- RGA, RGR, and RGE metric files and plots\n- compliance score tables\n- chatbot logs\n\n## Citation\n\nThis project is inspired by the SAFE AI and Rank Graduation Box framework:\n\n- [Babaei, G., Giudici, P., & Raffinetti, E. (2025). A Rank Graduation Box for SAFE AI. Expert Systems with Applications, 259, 125239.](https://www.sciencedirect.com/science/article/pii/S0957417424021067)\n\n- [Giudici, P., & Raffinetti, E. (2025). RGA: a unified measure of predictive accuracy. Advances in Data Analysis and Classification, 19, 67–93.](https://link.springer.com/article/10.1007/s11634-023-00574-2)\n\n- [Raffinetti, E. (2023). A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks. Quality & Quantity, 57(Suppl 2), 131–150.](https://link.springer.com/article/10.1007/s11135-023-01613-y)\n\n## Authors\n\n- [Yasamin Hosseinizadeh Sani](https://www.linkedin.com/in/yasamin-hosseinzadeh-sani-4b7429232/), University of Pavia\n- [Golnoosh Babaei](https://scholar.google.com/citations?user=KRdJkDMAAAAJ&hl=en&oi=ao), University of Pavia\n- [Paolo Giudici](https://scholar.google.com/citations?user=ogeVB1kAAAAJ&hl=en), University of Pavia\n","readmeExcerpt":"SAFE Agentic Credit System A modular agentic AI pipeline for credit-risk assessment with SAFE governance scoring, fairness auditing, robustness analysis, sensitivity analysis, multi-model compliance scoring, and an artifact-grounded chatbot. Overview This repository implements a SAFE agentic credit-lending system for machine learning governance. 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