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Explainability and Safety engender Trust. These require a model to exhibitconsistency and reliability. To achieve these, it is necessary to use andanalyze data and knowledge with statistical and symbolic AI methods relevant tothe AI application - neither alone will do. Consequently, we argue and seek todemonstrate that the NeuroSymbolic AI approach is better suited for making AI atrusted AI system. We present the CREST framework that shows how Consistency,Reliability, user-level Explainability, and Safety are built on NeuroSymbolicmethods that use data and knowledge to support requirements for criticalapplications such as health and well-being. This article focuses on LargeLanguage Models (LLMs) as the chosen AI system within the CREST framework. LLMshave garnered substantial attention from researchers due to their versatilityin handling a broad array of natural language processing (NLP) scenarios. Forexample, ChatGPT and Google's MedPaLM have emerged as highly promisingplatforms for providing information in general and health-related queries,respectively. Nevertheless, these models remain black boxes despiteincorporating human feedback and instruction-guided tuning. For instance,ChatGPT can generate unsafe responses despite instituting safety guardrails.CREST presents a plausible approach harnessing procedural and graph-basedknowledge within a NeuroSymbolic framework to shed light on the challengesassociated with LLMs.

Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety