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

• URL: http://arxiv.org/abs/2312.06798v1
• Date: Tue, 5 Dec 2023 06:13:55 GMT
• Title: Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
• Authors: Manas Gaur, Amit Sheth
• Abstract summary: We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework.
• Score: 11.933469815219544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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