The core elements of AI governance
Reference Guide
The Core Elements of AI Governance
As artificial intelligence (AI) intricately weaves into the fabric of our enterprises, the call for a principled approach to its governance has never been more pressing. The transformative potential of AI is immense, yet so are the ethical quandaries and operational challenges it introduces. As we stand at this crossroads, the delineation of robust governance frameworks becomes not just prudent but imperative for organizations aspiring to harness AI’s power responsibly.
This guide embarks on an exploration of the foundational elements that constitute effective AI governance. Through the establishment of cross-functional AI ethics boards, clear demarcation of roles and responsibilities in AI development, and the cultivation of AI principles that echo the core values of fairness, transparency, and ethics, organizations can navigate the AI landscape with confidence. This guide also delves into the critical spheres of data governance and model governance, providing actionable insights into managing data quality, biases, privacy, and security risks, as well as maintaining rigorous oversight over AI models.
Moreover, the guide underscores the significance of continuous monitoring and the agile resolution of issues, ensuring that AI systems remain aligned with organizational goals and ethical standards. Whether you are laying the groundwork for AI integration or seeking to refine your existing frameworks, this guide offers a beacon for your journey towards principled and effective AI governance.
Core Elements
Governance Structures
- Establish a cross-functional AI ethics board with representatives from leadership, legal, compliance, product, engineering, risk management, and other key stakeholders.
- Define clear roles and responsibilities for AI development, testing, monitoring, and controls. Separate model development from model validation.
- Develop an AI principles statement aligned to organizational values like ethics, fairness, transparency. Operationalize these principles with policies and controls.
- Invest in recruiting and developing AI talent with diverse expertise including data science, engineering, ethics, law, risk management.
Data Governance
- Document data sources and perform assessments for quality, biases, privacy and security risks. Establish data governance policies and corrective actions where needed.
- Carefully select data variables used in models, minimizing use of sensitive attributes.
- Validate any synthetic or augmented data for quality and representativeness.
Model Governance
- Maintain documentation on model components, metrics, test results, limitations, and monitoring.
- Define performance metrics aligned to goals and constraints covering accuracy, explainability, fairness and other priorities.
- Rigorously test models under representative conditions, document results transparently, implement human oversight controls.
Monitoring & Issue Resolution
- Establish an AI monitoring program including acceptable data/model drift thresholds and schedules tailored to risk levels.
- Require prompt corrective actions with traceability when monitoring reveals model underperformance or other issues.