AI Governance operating model
The AI Governance operating model includes the following AI Governance-specific asset types:
Asset type | Description | UUID |
---|---|---|
AI Model | An AI Model is a statistical model that is trained on datasets in order to make predictions on new data. | 00000000-0000-0000-0000-000000031402 |
AI Use Case | An AI Use Case is a specific application of artificial intelligence (AI) and AI Models trained on specific data in order to solve a business problem and deliver business value. Implementing an AI Use Case may result in automating a task, improve decision-making or develop new products and services. | 00000000-0000-0000-0000-000000031401 |
The following image shows the relations between the AI Governance asset types and other relevant asset types.
For instructions on how to create this diagram view in your Collibra environment, see Create an AI Governance operating model diagram view, at the bottom of this topic.
Descriptions, attributes, and relation types
Click the relevant tab below to see descriptions, attributes, and relation types for each asset type.
- AI Model
- AI Use Case
Description
A statistical model that is trained on datasets in order to make predictions on new data.
Relation types
Relation type | Head role / corole tail | Public ID |
---|---|---|
used in AI Use Case | AI Use Case uses / used in AI Model | AIUseCaseUsesAIModel |
trained by Asset | AI Model trained by / trains Asset | AIModelTrainedByAsset |
infers from Asset | AI Model infers from / used to infer Asset | AIModelInfersFromAsset |
has output Asset | AI Model has output / is output Asset | AIModelHasOutputAsset |
is provided by Vendor | AI Model is provided by / provides Vendor | AIModelProvidedByVendor |
uses AI Model | AI Model uses / is used by AI Model | AIModelUsesAIModel |
complies to Governance Asset | Asset complies to / applies to Governance Asset | AIAgentUsesAIModel |
Attributes
Attribute | Description | Public ID |
---|---|---|
Description | The description of the asset. This is typically a more verbose way to describe what the asset means. | Description |
Model Accuracy |
Model Accuracy refers to how well the model performs on a given task. Typically, this is defined by the proportion of correct predications made by the model. For example, if a model is used to classify emails as spam or not spam, and it classifies 90% of emails correctly, then the model accuracy is 90%. |
ModelAccuracy |
Model Precision |
Model Precision refers to how accurate positive model predictions are. Typically, this is defined as the proportion of predictions that are correct. For example, if a model is used to classify emails as spam, and it correctly classifies 95% of emails as spam, then the model has a precision of 0.95. |
ModelPrecision |
Mean Squared Error |
Mean Squared Error (MSE) refers to a model quality metric that measures the quality of the model’s predications. |
MeanSquaredError |
Mean Absolute Error |
Mean Absolute Error (MAE) refers to a model quality metric that evaluates the performance of regression models. |
MeanAbsoluteError |
Model Type |
Type of AI model. Values include: Generative AI, Classification, Regression, Computer Vision, Reinforcement Learning, and Image Classification. |
ModelType |
Retrain Cycle |
The frequency with which the model is retrained. |
RetrainCycle |
Feature Importance |
Feature Importance refers to how important a feature is to a machine learning model. It helps you to understand which features contribute the most to the model’s predictions. |
FeatureImportance |
Version | If this asset is versioned (manually or in an external system), this string represents the asset version. | Version |
Repository | Reference to the repository where the code behind the model is stored. | Repository |
Description
An AI use case is a specific application of AI and AI Models, trained on specific data in order to solve a business problem and deliver business value. Implementing an AI use case may result in automating a task, improving decision-making, or developing new products and services.
Relation types
Relation type | Head role / corole tail | Public ID |
---|---|---|
is assessed by Assessment Review | Asset is assessed by / assesses Assessment Review |
AssetIsAssessedByAssessmentReview |
uses AI Model | AI Use Case uses / used in AI Model |
AIUseCaseUsesAIModel |
infers from Asset | AI Use Case infers from / used to infer Asset |
AIUseCaseTransformsAsset |
trained by Asset | AI Use Case trained by / trains in Asset |
AIUseCaseTrainedByAsset |
has output Asset | AI Use Case has output / is output Asset |
AIUseCaseHasOutputAsset |
complies to Governance Asset | Asset complies to / applies to Governance Asset | AssetCompliesToGovernanceAsset |
Attributes
Attribute | Description | Public ID |
---|---|---|
Description |
General description of the Use Case and its potential for the use of AI. |
Description |
Use Case Application |
Indicates that the AI use case will be used by an external audience or internally by your organization. |
UseCaseApplication |
Use Case Stage | The stage that your AI use case is in. For example: Ideation, Development of Data and Models, or Implementation and Monitoring. | UseCaseStage |
Business Case |
Refers to the business problem you want to solve with the AI use case. It focuses on describing a concrete business problem. For example, I'm a customer support manager and my team receives too many support tickets. |
BusinessCase |
Business Value |
Refers to how this AI use case can improve your organization. For example, reduce support tickets, bring in additional revenue, or mitigate risks. |
BusinessValue |
Business Sponsor |
Refers to the Business Owner or Executive Sponsor of the AI Use Case in your organization. |
BusinessSponsor |
Maintenance Cost |
Indicates the overall expected cost of running the Use Case over selected period of time. |
MaintenanceCost |
General Purpose AI |
Refers to whether the model used in your AI use case is using a General Purpose AI (GPAI). Some regulatory frameworks may impose additional transparency and risk mitigation requirements for GPAI based systems, sometimes referred to as foundation models. For example, large language models (LLMs). |
GeneralPurposeAI |
Third Party Model |
Refers to the vendor of your AI model(s) and what kind of model you are using. For example, Google Vertex. |
ThirdPartyModel |
Internal Model |
Refers to the existing or upcoming internally built model(s) your AI use case may use. |
InternalModel |
Training Data Description |
The training or re-retraining data used to teach the AI model(s). |
TrainingDataDescription |
Inference Data Description |
An explanation of the input or inference data the AI model(s) uses to create output data. For example, an image classification model uses images as input data and a language model uses text as input data. |
InferenceDataDescription |
Model Output |
An explanation of the output data that the AI model(s) is expected to create. For example, classification labels, descriptions, or complex probability predictions. |
ModelOutput |
Data Storage |
Refers to whether any data is stored, and if so, where and how the data is stored. For example, the prompts data is stored on the cloud. |
DataStorage |
Automation Level |
The nature and degree of automation of the AI use case. For example, are decisions going to be based solely on the automated output, or is human oversight possible or planned? |
AutomationLevel |
Model Monitoring |
Refers to how your organization will ensure the AI model is meeting accuracy and performance expectations. |
ModelMonitoring |
Legal Approval Date |
Date your legal team approved or rejected the AI use case. |
LegalApprovalDate |
Legal Approval Renewal Date |
Date of the expected periodical review of the Use Case’s approval. |
LegalApprovalRenewalDate |
Legal Description of Model |
Description of the AI model provided by your legal team. This may include any legal repercussions of processing the AI model within the context of the AI use case. |
LegalDescriptionofModel |
Security Protocols |
Any general security protocols that may result from the implementation of the AI use case. |
SecurityProtocols |
Data Retention Protocols |
Any data retention standards already in place or projected to be implemented for the AI use case. |
DataRetentionProtocols |
Data Privacy Risks |
Any data privacy risks that may result from processing the AI model within the context of this AI use case and either putting it on the market for external customer use or internal use. For example, is there the risk of a data breach or misuse of data? |
DataPrivacyRisks |
Data Privacy Risk Score | Number calculated from Risk Assessment. | DataPrivacyRiskScore |
Intellectual Property Risks |
Inherent Intellectual Property Risks resulting from processing AI Models within this use case and placing them on the market or putting into service for own use. Examples include copyright or patent infringement. |
IntellectualPropertyRisks |
Ethical Risks |
Any ethical risks that may result from processing the AI model within the context of this AI use case and either putting it on the market for external customer use or internal use. For example, is there the risk of a data breach or misuse of data? |
EthicalRisks |
Other Risks |
Other Risks resulting from processing AI Models within this use case and placing them on the market or putting into service for own use. Examples can include safety and reliability, security, social risks. |
OtherRisks |
Business Risks | A summary of the business risks associated with implementing the AI use case. For example, the expected financial loss resulting from significant disruptions to the business due to the complexity of operating the AI model. | BusinessRisks |
Overall Risk Analysis |
Details and the result of any risk analysis performed on the AI use case. |
OverallRiskAnalysis |
Overall Risk Rating |
Risk level calculated based on pre-defined thresholds in the default Risk Assessment. |
OverallRiskRating |
Transparency Disclosure Requirements |
Transparency Disclosure Requirements refers to identified requirements, if any, for AI use case transparency. For example, a Transparency Declaration that requires an organization to disclose the purpose of the AI model and any data used for training purposes. |
TransparencyDisclosureRequirements |
Protective Measures |
Any additional required or recommended actions that have been identified based on regulations, industry standards or dedicated frameworks. |
ProtectiveMeasures |
Asset statuses
Tip You can configure which initial status you want AI Use Case assets to have when they are registered, and the asset status progression – in other words, the lifecycle stages through which you want your AI use cases to evolve. For more information, go to Configure AI Use Case asset statuses and The lifecycle of an AI use case.
Status | Description |
---|---|
Ideation | The AI use case is just a concept, to be reviewed with regard to value and risks. It is not yet used in production. It can be considered a proof of concept. |
Development |
The AI use case is in, or is ready to enter, the development process. |
Monitoring | The AI use case in production, is actively used and monitored with regard to business value and risks. AI models are monitored with regard to performance, reliability, and accuracy. |
Archived |
The AI use case is no longer in production. It is archived for documentation and audit purposes. |
Rejected | After being assessed for its value and inherent risks, the use case was not approved and not put into production. |
Tip For information on editing the status of an asset, go to Edit an asset.
Create an AI Governance operating model diagram view
You can create an AI Governance diagram view, to visualize the operating model. The following procedure describes how to quickly create a new diagram view by copying and pasting the JSON code in the diagram view text editor.
Steps
- Open an asset page.
- Click the
Diagram tab.
The diagram is shown in the default diagram view. - Click
to add a new view.
- Click the Text tab, to switch to the diagram view text editor.
- Click Show me the JSON code below this procedure, to expand the code.
- Copy and paste the code in the diagram view text editor.
- Click Save.
- Edit the name and description of the diagram view, to suit your needs.
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