Governing assets with watsonx.governance

Develop a comprehensive governance solution

Using watsonx.governance, you can extend the best practices of AI governance from predictive machine learning models to generative AI while monitoring and mitigating the risks associated with models, users, and data sets. The benefits of this approach include:

• Responsible AI: extend the practices of responsible AI from governing predictive machine learning models to the use of generative AI with any foundation or model provider.
• Explainability: Use automation to improve transparency and explainability for tracked models. Use tools for detecting and mitigating risks that are associated with AI.
• Transparent and regulatory policies: Mitigate AI risks by tracking the end-to-end AI lifecycle to aid compliance with internal policies and external regulations for enterprise-wide AI solutions.

Use the AI risk atlas as a guide

Start your governance journey by reviewing the AI risk atlas to learn about the potential risks of working with AI models. The AI risk atlas provides a guide to understanding some of the risks of working with AI models, including generative AI, foundation models, and machine learning models. In addition to describing potential risks, it provides real-world context. It is intended as an educational resource and is not meant as a prescriptive tool.

Components of watsonx.governance

Watsonx.governance includes these tools for addressing your governance needs in an integrated solution:

• Watson OpenScale provides tools for configuring monitors that evaluate your deployed assets against thresholds you specify. For example, you can configure threshold that alerts you when predictive machine learning models perform under a specified threshold for fairness in monitored outcomes, or drift from accuracy. Alerts for foundation models can warn you when a threshold is breached for the presence of hateful or abusive language or the detection of personal identifiable information. A Model Health monitor provides real-time performance tracking for deployed models.
• AI Factsheets collects the metadata for machine learning models and prompt templates you explicitly track. Develop AI use cases to gather all of the information for managing a model or prompt template from the request phase through development and into production. Manage multiple versions or a model, or compare different approaches to solving a business problem within a use case. Factsheets display information about the models including creation information, data that is used, and where the asset is in the lifecycle. A common model inventory dashboard gives you a view of all tracked assets, or you can view the details of a particular model, all in service of meeting policy and compliance goals.

Governance in action

This illustration depicts a typical governance flow, starting with defining an AI use case to solve a business problem and requesting an AI asset, such as a model or prompt template, to solve the problem. The figure shows the various roles that might be involved in the flow, starting with a model owner who defines the problem, then moving from the developer who builds the asset, to a validator who tests it. In the next step, a risk officer might review and approve the solution, hand it off to an ML Ops engineer to deploy it, and then deliver it to an App developer who can monitor the asset in production. Your approach might combine some of these roles.

Extend governance with Governance console

Optionally integrate with IBM OpenPages Model Risk Governance to manage governance tasks and activity from a common dashboard. You can also use OpenPages to develop workflows that support your governance processes.

Use a Risk Assessment questionnaire to assess the potential risk of your AI solution and assign a risk score.

For more information, see Managing risk and compliance with Governance console.

Extend governance with watsonx.ai

To create an end-to-end experience for developing assets and then adding them to governance, use watsonx.ai with watsonx.governance. Watsonx.ai extends the watsonx.ai Studio and watsonx.ai Runtime services to work with foundation models, including capabilities for saving prompt templates for a curated collection of large language model assets.

For more information on watsonx.ai, see:

• Overview of IBM watsonx as a Service
• Signing up for IBM watsonx as a Service

Next steps

• Plan your governance strategy
• Follow the steps in IBM watsonx.governance offering plan options to provision Watson OpenScale with AI Factsheets.