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

1. Technical Specifications

Table 1 provides the full list of AI Modules (AIM) specified by CAE-USC V2.4 with links to the pages dedicated to Data Types. Each of these includes Definition, Functional Requirements, Syntax, Semantics, Conformance Testing, and Performance Assessment.

All AIMs specified by CAE-USC V2.3 are superseded by those specified by CAE-USC V2.4. AIMs specified by CAE-USC V2.4 may still be used if their version is explicitly indicated.

Table 1 – Data Types specified by CAE-USC V2.4

Acronym Name JSON Acronym Name JSON
CAE-DLS Damaged List File CAE-ELS Editing List File
CAE-IRR Irregularity File File CAE-MAG Microphone Array Geometry File
CAE-MAS Multichannel Audio Stream File

2 Conformance testing

The Conformance a Data instance conforms with CAE-USC V2.4 is expressed by one of the two statements:

  1. “Data conforms with the relevant (Non-MPAI) standard” – for Data.
  2. “Data validates against the Data Type Schema” – for Data Object.

The latter statement implies that:

  1. Any Sub-Type of the Data conforms with the relevant Sub-Type specification of the applicable Qualifier.
  2. Any Content and Transport Format of the Data conform with the relevant Format specification of the applicable Qualifier.
  3. Any Attribute of the Data
    1. Conforms with the relevant (Non-MPAI) standard – for Data, or
    2. Validates against the Data Type Schema – for Data Object.

Note that at this stage the CAE-USC V2.4 specifies Conformance Testing only for some Data Types.

3. Performance Assessment

Performance is an umbrella term used to describe a variety of attributes – some specific of the application domain served by a specific Data Type. Therefore, Performance Assessment Specifications provide methods and procedures to measure how well a Data instance represents an original Data entity. Performance of an Implementation includes methods and procedures for all or a subset of the following characteristics:

  1. Quality– for example, how well a Scene Descriptors instance represent a scene.
  2. Bias: – for example, how dependent on specific features of the training data is the inference represented by the Data instance.
  3. Legality– for example, whether the Data instance was produced in a jurisdiction at a time by an AIM that complies with the relevant a regulation, e.g., the European AI Act.
  4. Ethics – for example, the data instance complies to a target ethical standard.

Note that at this stage the CAE-USC V2.4 specifies Performance Assessment only of some Data Types.

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