Challenges of AI “Hallucinations” in Trade Advisory Services

Artificial Intelligence (AI) has upgraded numerous sectors, including trade advisory services, offering the potential to enhance decision-making and improve efficiency. However, despite its multiple benefits, AI systems, particularly Large Language Models (LLMs) such as GPT, face a critical issue known as “hallucinations.” Hallucinations refer to the phenomenon where AI systems generate information that may sound plausible but is completely false or misleading. This can be especially problematic in fields such as trade advisory, where accuracy, precision, and reliability are paramount. For example, AI could generate inaccurate market predictions, regulatory information, or trade data, resulting in costly mistakes for businesses or even entire economies. These insights are drawn from the research “The Use of Artificial Intelligence in Trade Advisory Services” (2024), which was conducted as part of the Open Innovation Policy Fellowship in collaboration with Brunel University of London, under the supervision of ELOQUENCE’s partner Professor Tatiana Kalganova.

The Impact of Hallucinations on Trade Advisory Services

In trade advisory, AI models support decision-making, particularly in areas such as market trends analysis, regulatory compliance, and financial forecasting. These systems process large amounts of data and deliver insights much faster than traditional methods, making them an invaluable tool for businesses involved in international trade.

However, when AI generates hallucinated content, the results can be disastrous. For instance, in the context of trade, AI might produce inaccurate import/export regulations or suggest incorrect trade strategies based on faulty data. Inaccurate advice can lead to financial losses, penalties for non-compliance with trade laws, or even damaged reputations for companies relying on AI for critical decisions.

Why Does AI Hallucinate?

The root cause of AI hallucinations lies in the model’s architecture. AI systems like GPT are trained on massive datasets, which include vast amounts of publicly available information from diverse sources. While this data helps the AI model generate sophisticated responses, it is not always guaranteed to be accurate, complete, or up-to-date. Furthermore, LLMs like GPT do not possess inherent knowledge verification mechanisms to cross-check the facts they generate, making them prone to errors.

Another factor contributing to hallucinations is that these models rely on patterns and probability rather than understanding the underlying context or meaning of the information. This can cause the AI to “make up” information when it encounters gaps or ambiguities in its training data. As a result, even though the AI output may sound confident, it can be entirely fabricated or misleading.

Addressing the Hallucination Problem in Trade Advisory

While AI models are not perfect, solutions exist to mitigate the risk of hallucinations and improve the reliability of AI systems in trade advisory. Some approaches include:

  • Human-in-the-loop (HITL) systems,
  • Verification systems,
  • Explainable AI (XAI),
  • Regular model updates,
  • Domain-specific training.

AI’s potential to enhance trade advisory services is undeniable, but it’s clear that hallucinations pose a serious risk to the accuracy and reliability of AI outputs. In fields such as trade, where precision is essential, relying on AI without safeguards can lead to costly errors. By incorporating human expertise, building verification systems, and ensuring transparency, AI’s benefits can be fully harnessed while minimizing the risk of misleading or false information.