IEEE P2894/D8, Aug 2023
IEEE Draft Guide for an Architectural Framework for Explainable Artificial Intelligence
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- Language: English
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About This Item
IEEE P2894/D8, Aug 2023 is a draft guide for an architectural framework for explainable artificial intelligence, focused on computing and processing applications. It addresses how XAI systems can be organized, described, and evaluated so that their outputs are more understandable to users and stakeholders. For teams working on AI-enabled software, this draft can help align design choices with clearer interpretability, traceability, and communication of model behavior across technical workflows.
About IEEE P2894/D8, Aug 2023
This draft guide is intended to support architectural thinking for explainable AI by outlining a framework that can be used when developing or assessing computing systems with AI components. IEEE P2894/D8, Aug 2023 is relevant where explanations, model transparency, and system-level understanding are important to engineering decisions. It may help define how explanation mechanisms fit into the broader AI architecture, including the relationship between data, model behavior, and user-facing outputs in technical environments.
Where is IEEE P2894/D8, Aug 2023 used?
IEEE P2894/D8, Aug 2023 is most relevant in software and computing environments where AI results need to be interpretable for review, validation, or operational use. Typical use cases may include decision-support applications, analytics platforms, automated classification systems, and other AI-enabled workflows that require explanation of outputs. It is also useful in engineering teams that design XAI features for internal tools, regulated workflows, or systems where users need insight into why a model produced a given result.
Importance in practice
In practice, this draft guide can support more consistent design and review of explainable AI capabilities. IEEE P2894/D8, Aug 2023 may help reduce ambiguity in how explanations are generated, presented, and assessed, which is valuable for compliance efforts, testing, and design control. For organizations using AI in critical or high-trust settings, a clearer architectural framework can improve consistency, support risk reduction, and make it easier to compare implementation choices across projects.
- Architectural framework for XAI
- Computing and processing focus
- Explanation and transparency concepts
- Design and validation support
- Draft guide status
- Publication Date: 2023
- Standard Status: Inactive
- Publisher: IEEE
- Subject: Computing and Processing
- Official IEEE: Doi link
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