Responsible Data Science Policy Framework

We developed and maintain an open-source policy framework for responsible data science and AI practices. The framework provides a structured approach to governing data science and AI within organizations, covering the full lifecycle from data collection through model deployment and monitoring.

What the framework covers

• Data governance — Collection, storage, access, quality, and retention policies
• Model development — Training data requirements, bias testing, documentation standards
• Deployment and monitoring — Production safeguards, drift detection, incident response
• Risk management — Risk assessment methodology, escalation procedures, audit trails
• Compliance alignment — Mapped to NIST AI RMF, ISO 42001, EU AI Act, and GDPR requirements

How organizations use it

The framework is designed for adoption and customization. Organizations use it as a starting point for their own AI governance policies, adapting the sections relevant to their industry, regulatory environment, and risk tolerance.

If you need help implementing or customizing the framework for your organization, our AI governance team can help.