Chatbot hallucinations: explained, examined and solved

Imagine asking a chatbot for help, only to find that its answer is inaccurate, even fabricated; this isn’t just a hypothetical scenario, it’s a reality that highlights the need to address the phenomenon of chatbot hallucinations

Niv Hertz|
To understand this concept in detail, we need to review a real-world case of chatbot hallucinations. What is a chatbot hallucination?
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Chatbot hallucination occurs when an AI-driven chatbot generates responses that are false or misleading. While similar to AI hallucinations, chatbot hallucinations specifically refer to instances within conversational AI interfaces.
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AI carries inherent risks
AI carries inherent risks
AI carries inherent risks
(Photo: Shutterstock)
These errors can stem from:
  • Knowledge base limitations
  • Bad user queries
  • Poor retrieval in RAG chatbots
  • Gaps in the AI’s learning algorithms
The distinction lies in the interaction. Chatbot hallucinations directly impact user experience. The AI application’s inaccurate output often leads to confusion or misinformed decisions. Let's look at the Air Canada chatbot hallucination. In this case, a grieving passenger turned to Air Canada’s AI-powered chatbot for information on bereavement fares and received inaccurate guidance.
The chatbot indicated that the passenger could apply for reduced bereavement fares retroactively. However, this claim directly contradicted the airline’s official policy. Misinformation led to a small claims court case, where the tribunal awarded the passenger damages. It acknowledged the chatbot’s failure to provide reliable information and the airline’s accountability for its AI’s actions.
Who’s to blame? This incident didn’t just spotlight the immediate financial and reputational repercussions for Air Canada. It also sparked broader discussions about the reliability of AI-driven customer service solutions and the accountability of their creators.
Air Canada argued that the chatbot is liable for the mistake. This, however, didn’t hold up in civil court. The tribunal’s decision highlighted a notable expectation: companies must ensure their AI systems provide accurate information.
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Air Canada airliner
Air Canada airliner
Air Canada airliner
(Photo: Air Canada)
This case emphasizes the necessity of rigorous testing, continuous detection and safety, and clear communication strategies. It underscores the balance between leveraging AI innovation and maintaining accuracy in customer interactions.
What’s the impact? The ramifications of the Air Canada chatbot hallucination extend beyond one legal ruling. They raise questions about reliability and the legal responsibilities of companies deploying AI apps. Businesses that rely on AI to interact with customers, must make sure that their apps are advanced and drive value, but also accountable for their output.

Mitigate chatbot hallucinations in real-time with AI guardrails

The case of Air Canada underscores the need for such a solution. With AI guardrails, the chatbot could have been subjected to real-time checks against company policies. Guardrails would have flagged the misleading bereavement fare information before it impacted the customer.
Those guardrails are a robust layer of protection around generative AI applications. It is designed to: mitigate hallucinations, prevent prompt injections, block data leakage and flag inappropriate responses.
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Chatbot hallucinations can provide fabricated or inaccurate information
(Photo: DALL-E3 photo generator)
Guardrails promote safety and trust while offering total control over your AI-powered chatbot’s performance. They can also be customized to the company GenAI application’s needs.
By integrating guardrails into AI chatbots, companies can reduce the risk of hallucinations. They ensure the chatbot’s responses align with factual information and company policies.
Niv HertzNiv HertzAporia
In conclusion, the injection of AI in customer service, while transformative, carries inherent risks. The Air Canada chatbot incident clearly illustrates this. Chatbot hallucinations can severely undermine user trust. They can also lead to financial and reputational damages. Implementing preventive measures is key to avoiding such cases in the future.
Niv Hertz is director of AI at Aporia, a leading AI control platform trusted by both emerging tech startups and established Fortune 500 companies to guarantee the privacy, security and reliability of AI applications.
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