EC Artificial Intelligence Act
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  • EC Artificial Intelligence Act
  • EC AIA - Compliance Requirements
  • Article 09 - Risk Management System (ART09)
    • 09.01 - Risk Management System in Place
    • 09.02 - Risk Management System Capabilities and Process
    • 09.03 - Risk Management Measures
    • 09.04 - Testing
    • 09.05 - Residual Risks
    • 09.06 - Consideration of Children
    • 09.07 - Credit Institutions
  • Article 10 - Data Governance (ART10)
    • 10.01 - Define Sets
    • 10.02 - Dataset Governance Policies
    • 10.03 - Dataset Design Choices
    • 10.04 - Data Source Information
    • 10.05 - Dataset Annotations Information
    • 10.06 - Dataset Labels Information
    • 10.07 - Dataset Cleaning
    • 10.08 - Dataset Enrichment
    • 10.09 - Dataset Aggregation
    • 10.10 - Dataset Description, Assumptions and Purpose
    • 10.11 - Dataset Transformation Rationale
    • 10.12 - Dataset Bias Identification
    • 10.13 - Dataset Bias Mitigation
    • 10.14 - Dataset Bias Analysis Action and Assessment
    • 10.15 - Dataset Gaps and Shortcomings
    • 10.16 - Dataset Bias Monitoring - Ongoing
    • 10.17 - Dataset Bias Special/Protected Categories
  • Article 11 - Technical Documentation (ART11)
    • 11.01 - Technical Documentation Generated
    • 11.02 - Additional Technical Documentation
    • 11.03 - Technical Details
    • 11.04 - Development Steps and Methods
    • 11.05 - Pre-trained or Third Party Tools/Systems
    • 11.06 - Design Specification
    • 11.07 - System Architecture
    • 11.08 - Computational Resources
    • 11.09 - Data Requirements
    • 11.10 - Human Oversight Assessment
    • 11.11 - Pre Determined Changes
    • 11.12 - Continuous Compliance
    • 11.13 - Validation and Testing
    • 11.14 - Monitoring, Function and Control
    • 11.15 - Risk Management System
    • 11.16 - Changes
    • 11.17 - Other Technical Standards
    • 11.18 - Ongoing Monitoring System
    • 11.19 - Reports Signed
    • 11.20 - Declaration of Conformity
  • Article 12 - Record Keeping (ART12)
    • 12.01 - Logging Capabilities
    • 12.02 - Logging Traceability
    • 12.03 - Logging - Situations that may cause AI Risk
    • 12.04 - Logging - Biometric Systems Requirements
  • Article 13 - Transparency and provision of information to user (ART13)
    • 13.01 - Transparency of the AI System
    • 13.02 - Instructions for Use
  • Article 14 - Human Oversight (ART14)
    • 14.01 - Human Oversight mechanism
    • 14.02 - Human Oversight details
    • 14.03 - Human Oversight - Biometric Identification Systems
  • Article 15 - Accuracy, Robustness and Cybersecurity (ART15)
    • 15.01 - Accuracy Levels
    • 15.02 - Robustness Assessment
    • 15.03 - Continuous Learning Feedback Loop Assessment
    • 15.04 - Cyber Security Assessment
  • Article 17 - Quality Management System (ART17)
    • 17.01 - Quality Management System in Place
    • 17.02 - Compliance Strategy Stated
    • 17.03 - Design processes
    • 17.04 - Development and QA processes
    • 17.05 - Test and Validation Procedures
    • 17.06 - Technical Standards
    • 17.07 - Data Management Procedures
    • 17.08 - Risk Management System
    • 17.09 - Ongoing Monitoring System
    • 17.10 - Incident Reporting Procedures
    • 17.11 - Communications with Competent Authorities
    • 17.12 - Record Keeping Procedures
    • 17.13 - Resource Management Procedures
    • 17.14 - Accountability Framework
  • Article 61 - Post Market Monitoring System (ART61)
    • 61.01 - Post Market Monitoring System in Place
    • 61.02 - Data Collection Assessment
    • 61.03 - Post Market Monitoring Plan
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Article 10 - Data Governance (ART10)

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Last updated 2 years ago

Article 10 deals with the data and data governance practices required for EC AIA compliance. All activities related to data are detailed as part of this article. In this article, on the Seclea Platform there are seventeen defined categories with relevant checks.

Following is the article text with relevant category numbers (10.##) from the Seclea Platform.

High-risk AI systems which make use of techniques involving the training of models with data shall be developed on the basis of training, validation and testing data sets ().

Training, validation and testing data sets shall be subject to appropriate data governance and management practices (). Those practices shall concern in particular:

  1. the relevant design choices ()

  2. data collection ()

  3. relevant data preparation processing operations, such as annotation (), labelling (), cleaning (), enrichment () and aggregation ()

  4. the formulation of relevant assumptions, notably with respect to the information that the data are supposed to measure and represent (10.10, )

  5. a prior assessment of the availability, quantity and suitability of the data sets that are needed (, )

  6. examination in view of possible biases ()

  7. the identification of any possible data gaps or shortcomings, and how those gaps and shortcomings can be addressed ()

To the extent that it is strictly necessary for the purposes of ensuring bias monitoring, detection and correction in relation to the high-risk AI systems (), the providers of such systems may process special categories of personal data () referred to in Article 9(1) of Regulation (EU) 2016/679, Article 10 of Directive (EU) 2016/680 and Article 10(1) of Regulation (EU) 2018/1725, subject to appropriate safeguards for the fundamental rights and freedoms of natural persons, including technical limitations on the re-use and use of state-of-the-art security and privacy-preserving measures, such as pseudonymisation, or encryption where anonymisation may significantly affect the purpose pursued.

Below is the list of controls/checks part of Article 10.

10.01
10.02
10.03
10.04
10.05
10.06
10.07
10.08
10.09
10.11
10.12
10.13
10.14
10.15
10.16
10.17
10.01 - Define Sets
10.02 - Dataset Governance Policies
10.03 - Dataset Design Choices
10.04 - Dataset Source Information
10.05 - Dataset Annotations Information
10.06 - Dataset Labels Information
10.07 - Dataset Cleaning
10.08 - Dataset Enrichment
10.09 - Dataset Aggregation
10.10 - Dataset Description, Assumptions and Purpose
10.11 - Dataset Transformation Rationale
10.12 - Dataset Bias Identification
10.13 - Dataset Bias Mitigation
10.14 - Dataset Bias Analysis Action and Assessment
10.15 - Dataset Gaps and Shortcomings
10.16 - Dataset Bias Monitoring - Ongoing
10.17 - Dataset Bias Special/Protected Categories