OECD AI Principles
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  • OECD AI Principles
  • 1. Inclusive growth, sustainable development and well-being (ISW)
    • ISW01 - AI Governance
    • ISW02 - Responsible AI Policy
    • ISW03 - AI Oversight Process
  • 2. Human-centred values and fairness (HVF)
    • HVF01 - Define Sets
    • HVF02 - Human Oversight Mechanism
    • HVF03 - Human Oversight - Biometric Identification Systems
    • HVF04 - Human Oversight Details
    • HVF05 - Dataset Governance Policies
    • HVF06 - Dataset Design Choices
    • HVF07 - Dataset Source Information
    • HVF08 - Dataset Annotations Information
    • HVF09 - Dataset Labels Information
    • HVF10 - Dataset Cleaning
    • HVF11 - Dataset Enrichment
    • HVF12 - Dataset Aggregation
    • HVF13 - Dataset Description, Assumptions and Purpose
    • HVF14 - Dataset Transformation Rationale
    • HVF15 - Dataset Bias Identification
    • HVF16 - Dataset Bias Mitigation
    • HVF17 - Dataset Bias Analysis Action and Assessment
    • HVF18 - Dataset Gaps and Shortcomings
    • HVF19 - Dataset Bias Monitoring - Ongoing
    • HVF20 - Dataset Bias Special/Protected Categories
  • 3. Transparency and Explainability (TAE)
    • TAE01 - Technical Documentation Generated
    • TAE02 - Additional Technical Documentation
    • TAE03 - Technical Details
    • TAE04 - Development steps and methods
    • TAE05 - Pre-trained or Third party tools/systems
    • TAE06 - Design specification
    • TAE07 - System Architecture
    • TAE08 - Computational Resources
    • TAE09 - Data Requirements
    • TAE10 - Human Oversight Assessment
    • TAE11 - Pre Determined Changes
    • TAE12 - Continuous Compliance
    • TAE13 - Validation and Testing
    • TAE14 - Monitoring, Function and Control
    • TAE15 - Risk Management System
    • TAE16 - Changes
    • TAE17 - Other Technical Standards
    • TAE18 - Ongoing Monitoring System
    • TAE19 - Reports Signed
    • TAE20 - Transparency of the AI System
    • TAE21 - Instructions for Use
  • 4. Accuracy, Robustness and Cybersecurity (ARC)
    • ARC01 - Accuracy Levels
    • ARC02 - Robustness Assessment
    • ARC03 - Continuous Learning Feedback Loop Assessment
    • ARC04 - Cyber Security Assessment
  • 5. Accountability (ACC)
    • ACC01 - Logging Capabilities
    • ACC02 - Logging Traceability
    • ACC03 - Logging - Situations that may cause AI Risk
    • ACC04 - Logging - Biometric systems requirements
    • ACC05 - Details of Off-the-Shelf AI/ML Components
    • ACC06 - Evaluation Process of Off-the-Shelf Components
    • ACC07 - Quality Control Process of Off-the-Shelf Components
    • ACC08 - Internal Audit Reports
    • ACC09 - Risk Management System in Place
    • ACC10 - Risk Management System capabilities and processes
    • ACC11 - Risk Management Measures
    • ACC12 - Testing
    • ACC13 - Residual Risks
    • ACC14 - Full Track of Mitigation Measures
    • ACC15 - Quality Management System in Place
    • ACC16 - Compliance Strategy stated
    • ACC17 - Design Processes
    • ACC18 - Development and QA (Quality Assurance) processes
    • ACC19 - Test and Validation Procedures
    • ACC20 - Technical Standards
    • ACC21 - Data Management Procedures
    • ACC22 - Risk Management System
    • ACC23 - Ongoing Monitoring System
    • ACC24 - Post Market Monitoring System in Place
    • ACC25 - Data Collection Assessment
    • ACC26 - Post Market Monitoring Plan
    • ACC27 - Incident Reporting Procedures
    • ACC28 - Communications with Competent Authorities
    • ACC29 - Record Keeping Procedures
    • ACC30 - Resource Management Procedures
    • ACC31 - Accountability Framework
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  1. 3. Transparency and Explainability (TAE)

TAE10 - Human Oversight Assessment

Assessment of human oversight measures and the technical measures required to facilitate that human oversight interpretation of the outputs.

Upload a document describing in detail your assessment of the human oversight measures, including an assessment of the technical measures needed to facilitate the interpretation of the outputs of AI systems by the users.

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Last updated 2 years ago