research-article

Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety

Authors:

Revathy Venkataraman,

Vedant KhandelwalAuthors Info & Claims

IEEE Internet Computing, Volume 26, Issue 5

Pages 76 - 84

https://doi.org/10.1109/MIC.2022.3182349

Published: 01 September 2022 Publication History

Abstract

AI has seen wide adoption for automating tasks in several domains. However, AI's use in high-value, sensitive, or safety-critical applications such as self-management for personalized health or personalized nutrition has been challenging. These require that the AI system follows guidelines or well-defined processes set by experts, community, or standards. We characterize these as process knowledge (PK). For example, to diagnose the severity of depression, the AI system should incorporate PK that is part of the clinical decision-making process, such as the Patient Health Questionnaire (PHQ-9). Likewise, a nutritionist's knowledge and dietary guidelines are needed to create food plans for diabetic patients. Furthermore, the BlackBox nature of purely data-reliant statistical AI systems falls short in providing user-understandable explanations, such as what a clinician would need to ensure and document compliance with medical guidelines before relying on a recommendation. Using the examples of mental health and cooking recipes for diabetic patients, we show why, what, and how to incorporate PK along with domain knowledge in machine learning. We discuss methods for infusing PK and present performance evaluation metrics. Support for safety and user-level explainability of the PK-infused learning improves confidence and trust in the AI system.

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Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety

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Published In

cover image IEEE Internet Computing

IEEE Internet Computing Volume 26, Issue 5

Sept.-Oct. 2022

78 pages

ISSN:1089-7801

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1089-7801 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 September 2022

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Cited By

Sheth APallagani VRoy K(2024)Neurosymbolic AI for Enhancing Instructability in Generative AIIEEE Intelligent Systems10.1109/MIS.2024.344112839:5(5-11)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/MIS.2024.3441128
Hakiri AGokhale AYahia SMellouli N(2024)A comprehensive survey on digital twin for future networks and emerging Internet of Things industryComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110350244:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110350
Roy KKhandelwal VGoswami RDolbir NMalekar JSheth AWilliams BChen YNeville J(2023)Demo alleviateProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i13.27085(16479-16481)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i13.27085
Jia YJohnson GArnold AHeflin J(2023)An Evaluation of Strategies to Train More Efficient Backward-Chaining ReasonersProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627564(206-213)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627564
Sheth ARoy KGaur M(2023)Neurosymbolic Artificial Intelligence (Why, What, and How)IEEE Intelligent Systems10.1109/MIS.2023.326872438:3(56-62)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/MIS.2023.3268724

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