ISO 42001 Foundation flashcards
ISO 42001 Foundation · Build recall with a focused review session.
32 cards. Read the term and the definition here. The deck at the top of this page is optional.
- Artificial intelligence system
- Engineered or machine-based system that infers, for explicit or implicit objectives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
- AI management system
- Management system used to establish an AI policy and AI objectives and the processes needed to achieve those objectives within an organization.
- AI policy
- Documented statement of top management's overall intentions and direction for artificial intelligence, including commitments to applicable requirements and continual improvement.
- Interested party
- Person or organization that can affect, be affected by, or perceive itself to be affected by a decision, activity, or AI system.
- AI risk
- Effect of uncertainty on AI-related objectives, where the effect can be a negative deviation such as harm or a positive deviation such as opportunity.
- AI risk assessment
- Process that identifies AI risk sources, analyzes potential consequences and likelihood, and evaluates the resulting level of AI risk against defined criteria.
- AI risk treatment
- Process to select and implement options that modify AI risk, followed by determining whether residual AI risk is acceptable.
- AI system impact assessment
- Structured process used to assess how an AI system may affect individuals, groups, and society, including human rights and broader societal consequences.
- Residual risk
- The level of AI risk that remains after risk treatment options have been selected and implemented.
- Documented information
- Information that an organization is required to control and maintain, together with the medium on which it is contained.
- Top management
- Person or group of people who directs and controls an organization at the highest level.
- Continual improvement
- Recurring activity that enhances performance, used in an AI management system to improve suitability, adequacy, and effectiveness over time.
- Effectiveness
- Extent to which planned activities are realized and planned results are achieved within the AI management system.
- Statement of applicability
- Documented statement listing applicable AI controls, justifying inclusions and exclusions, and recording implementation status for the management system.
- AIMS scope
- Documented boundaries and applicability of the AI management system, including the AI systems, processes, locations, and organizational units covered.
- Context of the organization
- Combination of internal and external issues relevant to an organization's purpose and that affect its ability to achieve intended AI management system results.
- Leadership commitment
- Top management's active accountability for AI management system effectiveness, including integrating requirements into processes and providing necessary resources.
- AI objectives
- Measurable results to be achieved for artificial intelligence, consistent with the AI policy, monitored, communicated, and updated as needed.
- Competence
- Ability to apply knowledge and skills to achieve intended results, determined for persons whose work affects AI performance or the management system.
- Awareness
- Condition in which persons doing work under the organization's control know the AI policy, their contribution, and implications of not conforming.
- Communication
- Process of determining what, when, with whom, and how to communicate information relevant to the AI management system.
- Operational planning and control
- Planning and controlling processes needed to meet AI management system requirements and implement planned actions, including criteria and control of outsourced processes.
- Monitoring and measurement
- Determining what to monitor, methods used, timing, and when results are analyzed and evaluated for AI management system performance.
- Internal audit
- Systematic, independent, and documented process for obtaining evidence and objectively evaluating the AI management system against audit criteria.
- Management review
- Review by top management at planned intervals to confirm the AI management system remains suitable, adequate, and effective.
- Nonconformity
- Documented failure to meet a specified requirement of the AI management system or of the ISO/IEC 42001 standard.
- Corrective action
- Action taken to eliminate the cause of a nonconformity and prevent its recurrence in the AI management system.
- AI system life cycle
- Stages of an AI system from design and development through verification, deployment, operation, monitoring, and eventual decommissioning.
- Verification
- Confirmation, through provision of objective evidence, that specified requirements for an AI system or its processes have been fulfilled.
- Validation
- Confirmation, through provision of objective evidence, that requirements for a specific intended use or application of an AI system have been fulfilled.
- Data provenance
- Record of the origin, custody, and processing history of data used to develop, train, or operate an AI system.
- Decommissioning
- Controlled withdrawal of an AI system from use, including handling of data, access, documentation, and dependent processes.