IEEE 3152-2024
IEEE Standard for Transparent Human and Machine Agency Identification
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- Language: English
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- Language: English
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About This Item
IEEE 3152-2024 is the standard for Transparent Human and Machine Agency Identification, offering guidance for computing and processing environments where it is important to distinguish whether actions, decisions, or outputs originate from a person or a machine system. It helps define clearer agency boundaries, which can support accountability, traceability, and more reliable interaction in complex digital workflows. For organizations working with automated systems, the standard can be a useful reference for design, implementation, and review.
Overview of IEEE 3152-2024
This technical document focuses on identifying and distinguishing human agency from machine agency in computing and processing contexts. IEEE 3152-2024 is relevant where systems may generate outputs, recommendations, or actions that need to be attributed accurately for operational, governance, or compliance purposes. By addressing transparency in agency identification, the standard may help support consistent interpretation across software tools, interfaces, and automated workflows. It is especially relevant when clear attribution affects control, review, or downstream decision handling.
Typical use cases
IEEE 3152-2024 may be used in software platforms, decision-support tools, workflow automation, and other computing systems where users need to know whether a result was produced by a human or by a machine process. It can be relevant in mixed-initiative environments, logging and audit functions, and interfaces that present machine-generated recommendations or human approvals. In practice, IEEE 3152-2024 can support clearer agency labeling in systems used for operations, oversight, or process documentation.
Why it matters
Transparent agency identification matters because it can reduce confusion, improve accountability, and support more consistent review of automated and human-in-the-loop processes. IEEE 3152-2024 may help teams define requirements for attribution, verification, and documentation so that system behavior is easier to understand and assess. That can be valuable during procurement, implementation, testing, and governance, particularly when organizations need dependable evidence of who or what initiated a given action.
- Human and machine agency identification
- Computing and processing context
- Transparency and attribution support
- Workflow and audit relevance
- Active English-language standard
- Publication Date: 2025
- Standard Status: Active
- Publisher: IEEE
- Subject: Computing and Processing
- Official IEEE: Doi link
- This Version: 3152 (2025)
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