The complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations.

Large Language Models (LLMs) are increasingly integrated into Knowledge Graph (KG) construction and augmentation pipelines to reduce manual effort and enable scalable knowledge extraction, completion, and reasoning. While this integration offers substantial benefits, it also introduces new forms of bias and unreliability that extend beyond those observed in standalone LLMs or traditional knowledge graphs. In particular, biases originating from language models, such as social and representational bias, hallucination, prompt sensitivity, and domain coverage limitations that interact with structural and content biases inherent to knowledge graphs, result in compounded distortions that propagate across the pipeline. This paper provides a structured and comprehensive analysis of bias in LLM-augmented knowledge graph systems. We first review bias mechanisms in LLMs and standalone KGs, and then examine how these biases interact and amplify during key stages of LLM-based entity extraction, relation generation, graph completion, and reasoning. Based on this analysis, we introduce a unified taxonomy that characterizes bias as a pipeline-level phenomenon rather than an isolated model. We further consolidate recent evaluation metrics adapted for LLM-generated graphs, including semantic and soft lexical measures. Additionally, we survey representative datasets and benchmarks used to study bias in LLMs, KGs, and hybrid LLM–KG systems and identify open research gaps in developing pipeline-aware evaluation frameworks. This work aims to support the design of more reliable, accurate, and fair LLM-augmented knowledge graphs for engineering and domain-specific applications.