// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
AI Governance
The rules, processes, and structures that guide how AI is developed, deployed, and used within an organization or society.
TECHNICAL DEFINITION
AI Governance encompasses the frameworks, policies, standards, and organizational structures established to guide the responsible development, deployment, and oversight of artificial intelligence systems, addressing ethical, legal, and societal implications.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- AI management
- AI oversight
- AI control
- AI stewardship
USAGE NOTE
Robust AI governance is essential for managing risks and ensuring compliance in large enterprises.
DEVELOPERS
Organizations developing technology related to AI Governance.
Credo AI offers an AI Governance Platform that helps organizations measure, monitor, and manage AI risks, compliance, and ethics across the AI lifecycle, ensuring responsible AI deployment and use.
IBM provides comprehensive AI governance solutions through its Watson Anywhere platform, focusing on MLOps, explainability, fairness, and lifecycle management to build trustworthy and responsible AI systems.
Microsoft Azure AI offers Responsible AI tools and capabilities, including the Responsible AI Dashboard and Fairlearn toolkit, to help developers and organizations build, deploy, and manage AI systems ethically and accountably.
Google Cloud AI provides responsible AI principles and tools, such as Explainable AI and What-If Tool, to enable developers to understand, evaluate, and mitigate potential issues in AI models, supporting robust AI governance.
TruEra develops a platform for AI observability and explainability, providing insights into model performance, bias, and stability, which are critical components for effective AI governance and risk management.
Fiddler AI offers an AI Observability Platform that helps enterprises monitor, explain, and analyze their AI models in production, ensuring performance, fairness, and compliance with governance standards.
Arthur AI provides an AI performance monitoring and explainability platform that helps organizations detect and diagnose issues like bias, drift, and performance degradation in AI models, essential for responsible AI governance.
Databricks, through its Lakehouse Platform and tools like MLflow, offers features that support MLOps and model governance, enabling lineage tracking, versioning, and secure deployment of machine learning models.