AI governance refers to the framework of principles, policies, and practices that guide the responsible development, deployment, and use of artificial intelligence systems. It aims to ensure AI technologies are ethical, transparent, and aligned with human values and societal norms.

What is an AI governance platform?

An AI governance platform is a comprehensive solution that helps organizations implement and manage their AI governance strategies. It typically includes tools for model tracking, bias detection, explainability, and compliance monitoring.

For example, a healthcare organization might use an AI governance platform to ensure their diagnostic AI models meet regulatory requirements and maintain patient privacy. The platform would provide audit trails, bias assessments, and model performance monitoring across different demographic groups.

What are the pillars of AI governance?

The pillars of AI governance form the foundation for responsible AI development and deployment. They typically include ethics, transparency, accountability, fairness, and privacy.

Ethics in AI governance involves ensuring AI systems align with moral principles and societal values. Transparency requires that AI decision-making processes be explainable and understandable. Accountability establishes clear responsibility for AI outcomes.

Fairness focuses on mitigating bias and ensuring equitable treatment across different groups. Privacy safeguards personal data used in AI systems.

What is the difference between responsible AI and AI governance?

Responsible AI and AI governance are closely related but distinct concepts. Responsible AI refers to the practice of developing and using AI systems in an ethical and socially beneficial manner. It's the 'what' and 'why' of ethical AI development.

AI governance, on the other hand, provides the 'how'. It's the framework and mechanisms that ensure responsible AI principles are actually implemented and adhered to within an organization.

Consider a facial recognition system. Responsible AI principles might dictate that it should be unbiased across different ethnicities. AI governance would provide the processes to test for bias, monitor performance, and ensure corrective actions are taken when issues are identified.

What are the basics of AI governance?

The basics of AI governance include establishing clear policies, implementing monitoring and auditing processes, and fostering a culture of responsible AI development.

Start by defining your organization's AI principles and ethical guidelines. Implement processes for risk assessment and impact analysis of AI projects. Establish mechanisms for ongoing monitoring and auditing of AI systems in production.

Crucially, invest in education and training to ensure all team members understand and can apply these governance principles in their daily work.

How does Shakudo support AI governance in MLOps workflows?

Shakudo's platform integrates AI governance principles directly into MLOps workflows. It provides tools for model versioning, lineage tracking, and performance monitoring, enabling organizations to maintain transparency and accountability throughout the AI lifecycle.

With Shakudo, teams can implement governance checks at every stage of the ML pipeline, from data preparation to model deployment. This ensures that AI systems developed on the platform adhere to established governance policies, facilitating responsible AI practices without sacrificing development speed or flexibility.