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

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.