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1. Technical Specification 2. Conformance Testing 3. Performance Assessment

1. Technical Specifications

Table 1 provides the links to the specifications and the JSON syntax of all Data Types specified by Technical Specification: Compression and Understanding of Financial Data (MPAI-CUI) – Company Performance Prediction (CUI-CPP) V2.0. All previously specified MPAI-CUI Data Types are superseded by those specified by V2.0. They may still be used by explicitly signalling their version.

Table 1 – Data Types specified by CUI-CPP V2.0

Financial Descriptors Financial Statements Governance Descriptors
Governance Statements Organisation Descriptors Prediction Horizon
Primary Default Descriptors Primary Discontinuity Descriptors Primary Risk Statements
Risk Taxonomy Secondary Risk Statements Secondary Risk Matrix

2     Conformance testing

A Data instance of a Data Type Conforms with CUI-CPP V2.0 if the JSON Data validate against the relevant CUI-CPP V2.0 JSON Schema and if the Data Conforms with the relevant Data Qualifier, if present. does not provide method for testing the Conformance of the Semantics of the Data instance to the CUI-CPP V2.0 specification.

Conformance testing can be performed by a human using a JSON Validator to verify the Conformance of the syntax of JSON Data to the relevant JSON Schema; and, if the Data has a Qualifier, to verify that the syntax of the Data conforms with the relevant values in the Data Qualifier. Alternatively, Conformance testing can be performed by software implementing the steps above.

3. Performance Assessment

Performance Assessment provides methods of assessing the performance of an Data instance. Performance may have various connotations, such as:

  1. Quality: Performance Assessment measures the quality of the Data instance using a metric that depends on the nature of the Data, e.g., the accuracy of identification of Visual Sources in a Visual Scene Geometry.
  2. Bias: Performance Assessment measures the disparity of treatment applied to the Data instance using a metric that depends on a bias related to certain attributes of the Data instance. For example, a systematic misidentification of an object.
  3. Legal compliance: Performance Assessment uses an appropriate metric to measure how well the Data instance complies with with a certain legal standard.

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