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  • Canada - Artificial Intelligence and Data Act (AIDA)
  • Data Governance (DG) of Anonymized and Non-Anonymized Data
    • DG01 - Define Sets
    • DG02 - Dataset Governance Policies
    • DG03 - Dataset Design Choices
    • DG04 - Dataset Source Information
    • DG05 - Dataset Annotations Information
    • DG06 - Dataset Labels Information
    • DG07 - Dataset Cleaning
    • DG08 - Dataset Enrichment
    • DG09 - Dataset Aggregation
    • DG10 - Dataset Description, Assumptions and Purpose
    • DG11 - Dataset Transformation Rationale
    • DG12 - Dataset Bias Identification
    • DG13 - Dataset Bias Mitigation
    • DG14 - Dataset Bias Analysis Action and Assessment
    • DG15 - Dataset Gaps and Shortcomings
    • DG16 - Dataset Bias Monitoring - Ongoing
    • DG17 - Dataset Bias Special/Protected Categories
    • DG18 - Dataset Anonymization Process
    • DG19 - Dataset Anonymization Assessment
  • Record Keeping (RK)
    • RK01 - Logging Capabilities
    • RK02 - Logging Traceability
    • RK03 - Logging - Situations that May Cause AI Risk
    • RK04 - Logging - Biometric systems requirements
  • Technical Documentation (TD)
    • TD01 - Technical Documentation Generated
    • TD02 - Additional Technical Documentation
    • TD03 - Technical Details
    • TD04 - Development Steps and Methods
    • TD05 - Pre-trained or Third-Party Tools/Systems
    • TD06 - Design Specification
    • TD07 - System Architecture
    • TD08 - Computational Resources
    • TD09 - Data Requirements
    • TD10 - Human Oversight Assessment
    • TD11 - Pre-Determined Changes
    • TD12 - Continuous Compliance
    • TD13 - Validation and Testing
    • TD14 - Monitoring, Function and Control
    • TD15 - Risk Management System
    • TD16 - Changes
    • TD17 - Other Technical Standards
    • TD18 - Ongoing Monitoring System
    • TD19 - Reports Signed
  • Transparency and Provision of Information to User (TPI)
    • TPI01 - Transparency of the High-Impact AI System
    • TPI02 - Instructions for Use
  • Risk Management System (RMS)
    • RMS01 - Risk Management System in Place
    • RMS02 - Risk Management System Capabilities and Process
    • RMS03 - Risk Management Measures
    • RMS04 - Testing
    • RMS05 - Residual Risks
    • RMS06 - Consideration of Children
  • Quality Management System (QMS)
    • QMS01 - Quality Management System in Place
    • QMS02 - Compliance Strategy
    • QMS03 - Design Processes
    • QMS04 - Development and QA Processes
    • QMS05 - Test and Validation Procedures
    • QMS06 - Technical Standards
    • QMS07 - Data Management Procedures
    • QMS08 - Risk Management System
    • QMS09 - Ongoing Monitoring System
    • QMS10 - Incident Reporting Procedures
    • QMS11 - Communications with Competent Authorities
    • QMS12 - Record Keeping Procedures
    • QMS13 - Resource Management Procedures
    • QMS14 - Accountability Framework
    • QMS15 - Human Oversight Mechanism
    • QMS16 - Human Oversight Details
    • QMS17 - Accuracy Levels
    • QMS18 - Robustness Assessment
    • QMS19 - Continuous Learning Feedback Loop Assessment
    • QMS20 - Cyber Security Assessment
  • Post Market Monitoring System (PMS)
    • PMS01 - Post Market Monitoring System in Place
    • PMS02 - Data Collection Assessment
    • PMS03 - Post Market Monitoring Plan
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  1. Technical Documentation (TD)

TD14 - Monitoring, Function and Control

PreviousTD13 - Validation and TestingNextTD15 - Risk Management System

Last updated 2 years ago

For compliance, a document that detailed information about the monitoring, functioning and control of the AI system, in particular concerning its capabilities and limitations in performance, including the degrees of accuracy for specific persons or groups of persons on which the system is intended to be used and the overall expected level of accuracy in relation to its intended purpose; the foreseeable unintended outcomes and sources of risks to health and safety, fundamental rights and discrimination gave the intended purpose of the AI system; the human oversight measures needed per including the technical measures put in place to facilitate the interpretation of the outputs of AI systems by the users; specifications on input data, as appropriate.

QMS