Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability,
  Explainability, and Safety
Manas Gaur Amit Sheth
Explainability and Safety engender Trust. These require a model to exhibit
consistency and reliability. To achieve these, it is necessary to use and
analyze data and knowledge with statistical and symbolic AI methods relevant to
the AI application - neither alone will do. Consequently, we argue and seek to
demonstrate that the NeuroSymbolic AI approach is better suited for making AI a
trusted AI system. We present the CREST framework that shows how Consistency,
Reliability, user-level Explainability, and Safety are built on NeuroSymbolic
methods that use data and knowledge to support requirements for critical
applications such as health and well-being. This article focuses on Large
Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs
have garnered substantial attention from researchers due to their versatility
in handling a broad array of natural language processing (NLP) scenarios. For
example, ChatGPT and Google's MedPaLM have emerged as highly promising
platforms for providing information in general and health-related queries,
respectively. Nevertheless, these models remain black boxes despite
incorporating 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-based
knowledge within a NeuroSymbolic framework to shed light on the challenges
associated with LLMs.