<- References Go to ToC AI Modules ->

1        Functions 2        Reference Model 3        CAV Input/Output Data
4       Functions of AI Workflows 5        I/O Data of AI Workflows 6        AIWs and JSON Metadata
7.      Reference Software 8.       Conformance Testing 9.       Performance Assessment

1       Functions

2       Reference Architecture

Technical Specification: Compression and Understanding of Industrial Data (MPAI-CUI) – Company Performance Prediction (CUI-CPP) V2.0 assumes that workflows are based on Technical Specification: AI Framework (MPAI-AIF) V2.1 specifying the standard AI Framework (AIF) that enables initialisation, dynamic configuration, execution, and control of AI Workflows (AIW) composed of interconnected AI Modules (AIM).

The CUI-CPP V2.0 AI-Workflow supersedes those specified by previous MPAI-CUI specifications. These can still be used if their version is explicitly indicated.

Figure 1 – Company Performance Prediction V2.0 Reference Model

3       I/O Data

Table 2 gives the input/output data of the CUI-CPP V2.0 AIW.

Table 1 – I/O data of CUI-CPP V2.0 AIW

Input data From Description
Prediction Horizon CUI-CPP user Prediction time in months.
Primary Risk Statements External source
Governance Statements Company
Financial Statements Company
Secondary Risk Statements Company

4     Functions of AI Modules

Table 2 gives the functions of all CUI-CPP V2.0 AIMs. Each link provides the AIM function, reference model, Input/Output Data, and JSON metadata.

Table 2 – Functions of CUI-CPP V2.0 AIMs

AIMs Function
CUI-GVA
CUI-FNA
CUI-RMG
CUI-CAP
CUI-PRP

5   I/O Data of AI Modules

Table 3 provides the link to the specified AIMs.

Table 3 – I/O Data of CUI-CPP V2.0 AIMs

Acronym Input Output
CUI-CAP Prediction Horizon
Primary Risk Statement
Governance Descriptors
Financial Descriptors		 Organisation Descriptors
Primary Default Descriptors
Primary Discontinuity Descriptors
CUI-FNA Financial Statements  Financial Descriptors
CUI-GVA Governance Statements
Financial Statements		 Governance Descriptors
CUI-PRP Primary Default Descriptors
Secondary Risk Matrix		 Primary Discontinuity Probability
CUI-RMG Secondary Risk Statements Secondary Risk Matrix

6    AIWs and JSON Metadata

Table 4 provides the links to the AIW and AIW specifications and to the JSON Metadata.

Table 4 – AIWs and JSON Metadata

AIW  AIMs Name JSON
CUI-CPP Company Performance Prediction X
CUI-CAP Company Assessment and Prediction X
CUI-FNA Financial Assessment X
CUI-GVA Governance Assessment X
CUI-PRP Prediction Result Perturbation X
CUI-RMG Risk Matric Generation X

7    Reference Software

As a rule, MPAI provides Reference Software implementing the Technical Specification released with the BSD-3-Clause licence and the following disclaimers:

  1. The purpose of the Reference Software is to demonstrate a working Implementation of an AIW, not to provide a ready-to-use product.
  2. MPAI disclaims the suitability of the Software for any other purposes that those of the MPAI-HMC Standard and does not guarantee that it is secure.
  3. Users shall verify that they have the right to use any third-party software required by the Reference Software Implementation.
  4. Users should note that the Reference Software Implementation may require the acceptance of licences from third-party repositories.

8    Conformance Testing

An implementation of an AI Workflow conforms with MPAI-HMC if it accepts as input and produces as output Data and/or Data Objects (Data and its Qualifier) conforming with those specified or referenced by MPAI-HMC.

The Conformance of an instance of a Data is to be expressed by a sentence like “Data validates against the Data Type Schema”. This means that:

  • Any Data Sub-Type is as indicated in the Qualifier.
  • The Data Format is indicated by the Qualifier.
  • Any File and/or Stream have the Formats indicated by the Qualifier.
  • Any Attribute of the Data is of the type or validates against the Schema specified in the Qualifier.

9    Performance Assessment

Performance is a multidimensional entity because it can have various connotations. Therefore, the Performance Assessment Specification provides methods to measure how well an AIW performs its function, using a metric that depends on the nature of the function, such as:

  1. Quality: the Performance of a Communicating Entities in Context AIW can measure how well the AIW responds to a question.
  2. Bias: Performance of a Communicating Entities in Context AIW can measure the quality of responses in dependence of the type of message received.
  3. Legal compliance: the Performance of an AIW can measure the compliance of the AIW to a regulation, e.g., the European AI Act.
  4. Ethical compliance: the Performance Assessment of an AIW can measure the compliance of an AIW to a target ethical standard.

<- References Go to ToC AI Modules ->