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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5. Accountability (ACC)

The OECD principle of Accountability states: the terms accountability, responsibility and liability are closely related yet different, and also carry different meanings across cultures and languages. Generally speaking, “accountability” implies an ethical, moral, or other expectation (e.g., as set out in management practices or codes of conduct) that guides individuals’ or organisations’ actions or conduct and allows them to explain reasons for which decisions and actions were taken. In the case of a negative outcome, it also implies taking action to ensure a better outcome in the future. “Liability” generally refers to adverse legal implications arising from a person’s (or an organisation’s) actions or inaction. “Responsibility” can also have ethical or moral expectations and can be used in both legal and non-legal contexts to refer to a causal link between an actor and an outcome.

Given these meanings, the term “accountability” best captures the essence of this principle. In this context, “accountability” refers to the expectation that organisations or individuals will ensure the proper functioning, throughout their lifecycle, of the AI systems that they design, develop, operate or deploy, in accordance with their roles and applicable regulatory frameworks, and for demonstrating this through their actions and decision-making process (for example, by providing documentation on key decisions throughout the AI system lifecycle or conducting or allowing auditing where justified).

Below is the list of controls/checks part of the Accountability (ACC)

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Last updated 2 years ago