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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OECD AI Principles

Next1. Inclusive growth, sustainable development and well-being (ISW)

Last updated 2 years ago

The OECD AI Principles promote innovative and trustworthy AI use that respects human rights and democratic values. Adopted in May 2019, they set standards for AI that are practical and flexible enough to stand the test of time. The template captures the baseline an AI project should meet to conform to the principles set by the OECD AI Principles.

According to the OECD website, AI is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. It is deployed in many sectors ranging from production, finance and transport to healthcare and security.

Alongside benefits, AI also raises challenges for our societies and economies, notably regarding economic shifts and inequalities, competition, transitions in the labour market, and implications for democracy and human rights.

The OECD has undertaken empirical and policy activities on AI in support of the policy debate over the past two years, starting with a Technology Foresight Forum on AI in 2016 and an international conference on AI: Intelligent Machines, Smart Policies in 2017. The Organisation also conducted analytical and measurement work that provides an overview of the AI technical landscape, maps economic and social impacts of AI technologies and their applications, identifies major policy considerations, and describes AI initiatives from governments and other stakeholders at national and international levels.

This work has demonstrated the need to shape a stable policy environment at the international level to foster trust in and adoption of AI in society. Against this background, the OECD Committee on Digital Economy Policy (CDEP) agreed to develop a draft Council Recommendation to promote a human-centric approach to trustworthy AI, that fosters research, preserves economic incentives to innovate, and applies to all stakeholders.

The OECD AI Principles include:

Inclusive growth, sustainable development and well-being
Human-centred values and fairness
Transparency and Explainability
Accuracy, Robustness and Cybersecurity
Accountability