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Copy file name to clipboardExpand all lines: schema/bom-1.5.proto
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@@ -57,6 +57,12 @@ enum Classification {
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CLASSIFICATION_CONTAINER=7;
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// A special type of software that provides low-level control over a devices hardware. Refer to https://en.wikipedia.org/wiki/Firmware
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CLASSIFICATION_FIRMWARE=8;
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// A runtime environment which interprets or executes software. This may include runtimes such as those that execute bytecode or low-code/no-code application platforms.
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CLASSIFICATION_PLATFORM=9;
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// A model based on training data that can make predictions or decisions without being explicitly programmed to do so.
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CLASSIFICATION_MACHINE_LEARNING_MODEL=10;
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// A collection of discrete values that convey information.
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CLASSIFICATION_DATA=11;
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}
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messageCommit {
@@ -119,6 +125,10 @@ message Component {
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repeatedEvidenceevidence=23;
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// Specifies optional release notes.
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optionalReleaseNotesreleaseNotes=24;
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// A model card describes the intended uses of a machine learning model, potential limitations, biases, ethical considerations, training parameters, datasets used to train the model, performance metrics, and other relevant data useful for ML transparency.
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optionalModelCardmodelCard=25;
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// This object SHOULD be specified for any component of type `data` and MUST NOT be specified for other component types.
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optionalComponentDatadata=26;
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}
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// Specifies the data classification.
@@ -195,6 +205,8 @@ enum ExternalReferenceType {
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EXTERNAL_REFERENCE_TYPE_BUILD_SYSTEM=14;
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// Specifies a way to contact the maintainer, supplier, or provider in the event of a security incident. Common URIs include links to a disclosure procedure, a mailto (RFC-2368) that specifies an email address, a tel (RFC-3966) that specifies a phone number, or dns (RFC-4501]) that specifies the records containing DNS Security TXT.
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EXTERNAL_REFERENCE_TYPE_SECURITY_CONTACT=15;
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// A model card describes the intended uses of a machine learning model, potential limitations, biases, ethical considerations, training parameters, datasets used to train the model, performance metrics, and other relevant data useful for ML transparency.
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EXTERNAL_REFERENCE_TYPE_MODEL_CARD=16;
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}
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enumHashAlg {
@@ -780,4 +792,181 @@ message Annotation {
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google.protobuf.Timestamptimestamp=4;
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// The textual content of the annotation.
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stringtext=5;
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}
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messageModelCard {
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// An optional identifier which can be used to reference the model card elsewhere in the BOM. Every bom-ref MUST be unique within the BOM.
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optionalstringbom_ref=1;
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// Hyper-parameters for construction of the model.
// References a data component by the components bom-ref attribute
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stringref=2;
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}
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}
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messageMachineLearningInputOutputParameters {
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// The data format for input/output to the model. Example formats include string, image, time-series
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optionalstringformat=1;
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}
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}
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messageQuantitativeAnalysis {
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// The model performance metrics being reported. Examples may include accuracy, F1 score, precision, top-3 error rates, MSC, etc.
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repeatedPerformanceMetricsperformanceMetrics=1;
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optionalGraphicsCollectiongraphics=2;
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messagePerformanceMetrics {
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// The type of performance metric.
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optionalstringtype=1;
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// The value of the performance metric.
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optionalstringvalue=2;
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// The name of the slice this metric was computed on. By default, assume this metric is not sliced.
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optionalstringslice=3;
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// The confidence interval of the metric.
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optionalConfidenceIntervalconfidenceInterval=4;
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messageConfidenceInterval {
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// The lower bound of the confidence interval.
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optionalstringlowerBound=1;
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// The upper bound of the confidence interval.
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optionalstringupperBound=2;
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}
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}
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}
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messageModelCardConsiderations {
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// Who are the intended users of the model?
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repeatedstringusers=1;
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// What are the intended use cases of the model?
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repeatedstringuseCases=2;
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// What are the known technical limitations of the model? E.g. What kind(s) of data should the model be expected not to perform well on? What are the factors that might degrade model performance?
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repeatedstringtechnicalLimitations=3;
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// What are the known tradeoffs in accuracy/performance of the model?
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repeatedstringperformanceTradeoffs=4;
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// What are the ethical (or environmental) risks involved in the application of this model?
Copy file name to clipboardExpand all lines: schema/bom-1.5.schema.json
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@@ -2037,16 +2037,18 @@
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"items" : {
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"oneOf" : [
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{
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"title": "Inline Component Dataset",
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"title": "Inline Component Data",
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"$ref": "#/definitions/componentData"
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},
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{
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"title": "Data Component Reference",
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"description": "References a data component by the components bom-ref attribute",
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"ref": {
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"type": "string",
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"title": "Reference",
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"description": "References a data component by the components bom-ref attribute"
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"additionalProperties": false,
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"properties": {
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"ref": {
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"type": "string",
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"title": "Reference",
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"description": "References a data component by the components bom-ref attribute"
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}
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}
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}
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]
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"additionalItems": false,
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"items": { "$ref": "#/definitions/risk" }
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},
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"fairnessAssessment": {
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"fairnessAssessments": {
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"type": "array",
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"title": "Fairness Assessment",
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"title": "Fairness Assessments",
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"description": "How does the model affect groups at risk of being systematically disadvantaged? What are the harms and benefits to the various affected groups?",
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"additionalItems": false,
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"items": {
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"additionalProperties": false,
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"properties": {
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"custodians": {
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"type": "array",
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"title": "Data Custodians",
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"description": "Data custodians are responsible for the safe custody, transport, and storage of data.",
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