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The dramatic surge of health misinformation on social media platforms poses a significant threat to public health, contributing to hesitancy in vaccines, delayed medical interventions, and the adoption of untested or harmful treatments. We present a novel, hybrid AI-driven framework designed for the real-time detection of health misinformation on social media platforms while prioritizing user privacy. The framework integrates the strengths of Large Language Models (LLMs), such as DistilBERT, with domain-specific Knowledge Graphs (KGs) to enhance the detection of nuanced and contextually dependent misinformation. LLMs excel at understanding the complexities of human language, while KGs provide a structured representation of medical knowledge, allowing factual verification and identification of inconsistencies. Furthermore, the framework incorporates robust privacy-preserving mechanisms, including differential privacy and secure data pipelines, to address user privacy concerns and comply with healthcare data protection regulations. Our experimental results on a dataset of Reddit posts related to chronic health conditions demonstrate the performance of this hybrid approach compared to models that only use text or KG, highlighting the synergistic effect of combining LLMs and KGs for improved misinformation detection.
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Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety
Abstract Explainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyzedataandknowledgewith 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 howConsistency,Reliability, user‐levelExplainability, andSafety 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. As examples, 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 generateunsafe responsesdespite 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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- Award ID(s):
- 2335967
- PAR ID:
- 10490817
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 45
- Issue:
- 1
- ISSN:
- 0738-4602
- Format(s):
- Medium: X Size: p. 139-155
- Size(s):
- p. 139-155
- Sponsoring Org:
- National Science Foundation
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