IEEE 2894-2024
$40.63
IEEE Guide for an Architectural Framework for Explainable Artificial Intelligence (Approved Draft)
| Published By | Publication Date | Number of Pages |
| IEEE | 2024 |
New IEEE Standard – Active. A new wave of artificial intelligence applications that offer extensive benefits to our daily lives has been led to by dramatic success in machine learning. The loss of explainability during this transition, however, means vulnerability to vicious data, poor model structure design, and suspicion of stakeholders and the general public–all with a range of legal implications. The study of explainable AI (XAI), which is an active research field that aims to make AI systems results more understandable to humans, has been called for by this dilemma. This is a field with great hopes for improving the trust and transparency of AI-based systems and is considered a necessary route for AI to move forward. A technological blueprint for building, deploying, and managing machine learning models, while meeting the requirements of transparent and trustworthy AI by adopting a variety of XAI methodologies, is provided by this guide. It defines the architectural framework and application guidelines for explainable AI, including: description and definition of XAI; the types of XAI methods and the application scenarios to which each type applies; and performance evaluation of XAI.
PDF Catalog
| PDF Pages | PDF Title |
|---|---|
| 1 | IEEE Std 2894™-2024 Front cover |
| 2 | Title page |
| 4 | Important Notices and Disclaimers Concerning IEEE Standards Documents |
| 8 | Participants |
| 9 | Introduction |
| 10 | Contents |
| 11 | 1. Overview 1.1 Scope 1.2 Purpose |
| 12 | 1.3 Word usage 2. Normative references 3. Definitions, acronyms, and abbreviations 3.1 Definitions |
| 13 | 3.2 Acronyms and abbreviations 4. XAI architectures 4.1 General |
| 14 | 4.2 Interpretability goals of AI |
| 15 | 4.3 Taxonomy of explainability |
| 17 | 4.4 Explanations properties |
| 20 | 4.5 XAI reference architecture |
| 22 | 5. XAI requirements 5.1 Explanation method properties |
| 23 | 5.2 Levels of detail of explanation |
| 24 | 6. XAI explanation assessment 6.1 General 6.2 Transparency explanation |
| 25 | 6.3 Post hoc explanation 6.4 Qualitative assessment |
| 34 | 6.5 Model-specific explanation methods |
| 39 | 6.6 Data-centric explanation methods |
| 41 | Annex A (informative) List of advantages and disadvantages of post-modeling evaluation methods |
| 42 | Annex B (informative) A case study of infidelity and sensitivity for gradient explanations B.1 Gradient |
| 43 | B.2 Finance |
| 45 | B.3 Health |
| 46 | B.4 Urban computing |
| 47 | B.5 Energy |
| 48 | B.6 Telecommunication |
| 51 | Annex C (informative) Bibliography |
| 55 | Back cover |
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