Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
This work addresses the need for trustworthy AI in critical domains like health, though it appears incremental as it builds on existing NeuroSymbolic approaches.
The paper tackles the problem of making AI systems more trustworthy by proposing the CREST framework, which uses NeuroSymbolic AI methods to enhance consistency, reliability, explainability, and safety in applications like healthcare, with a focus on addressing black-box issues in Large Language Models such as ChatGPT.
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.