No ChatGPT Isn't on Drugs: What Is AI Hallucination?
And How to Stop It From Happening
With the rise of AI, a growing list of terms – from esoteric to the layman – have been injected into our vocabulary, and among them is “hallucination.” Not hallucination, as in chasing the dragon, so to speak, but hallucination in reference to artificial intelligence.
In this article, we’ll explore what an AI hallucination is, why it happens, and how to prevent them from occurring. Keep reading to learn more.
What Is AI Hallucination?
In the simplest possible terms, AI hallucination refers to a phenomenon in which generative AI chatbots present false information as fact. However, the false AI response users receive usually does not appear incorrect, as it is stated with confidence and may even come after a string of verifiable information.
Why Does Artificial Intelligence Hallucinate?
Artificial intelligence (AI) hallucinates or produces fabricated information due to several factors:
Training Data Biases: AI models learn from large datasets, which can contain biases or inaccuracies. If the training data includes incorrect or misleading information, the AI model may generate outputs that align with those biases, resulting in hallucinations.
Overfitting: Sometimes, AI models become overly specialized in the training data they were exposed to. As a result, when given new or incomplete input, they may produce outputs that are imaginative and seemingly true but not grounded in reality.
Incomplete Information: When AI systems receive partial or ambiguous input, they may attempt to fill in the missing details by making assumptions or drawing upon patterns in the training data. This can lead to the generation of content that is not entirely accurate or realistic.
Complexity of the Real World: The real world is vast and complex, and AI models have limitations in comprehending its intricacies. When confronted with complex or nuanced scenarios, AI may struggle to generate factual or coherent responses, resulting in hallucinations.
Inherent Uncertainty: AI models often rely on statistical patterns and probabilities rather than absolute certainty. In situations where the probability distribution is uncertain or unclear, AI systems may generate speculative or inaccurate information.
Addressing and mitigating AI hallucination involves improving training data quality, refining model architectures, and implementing robust validation mechanisms to ensure the generated content aligns with reality.
Challenges Associated With AI Hallucinations
Though there’s much hysteria and controversy surrounding AI in the news, 50% of people are willing to trust the technology. This means that half of the population is at risk of falling for AI hallucinations and taking them as fact, particularly in the workplace. As a result, there are several challenges associated with AI hallucinations that should be considered.
Inaccurate Customer Support: As more companies utilize AI customer service chatbots instead of human support teams, there’s a likelihood that they could give customers incorrect information. For example, instead of handing the chat off to a human, the AI may opt to fill in its knowledge gaps with hallucinated information. This, of course, bodes negatively for both the customer and business.
Erosion of Trust: Although right now, 50% of people are willing to trust AI, this may be eroded as more people learn about hallucinations and how artificial intelligence works. Understanding the challenges and limitations related to AI could eventually lead to reduced trust in companies and governments that utilize AI technology.
Ineffective Marketing Content: When asking AI to gather data for marketing purposes, such as potential customer demographics, they may generate false customer profiles due to a lack of available information. This could result in businesses wrongly targeting customers, causing an overall slump in sales for businesses that rely on AI for marketing.
False Research: When using generative AI as a research tool, researchers may find themselves encountering false information that litters their research with falsehoods. This could lead to an erosion of academic and intellectual honesty.
Have you ever experienced AI hallucination?
How to Prevent AI Hallucinations
It should be obvious by now that AI hallucinations present a real threat to the integrity of the responses generated by chatbots. However, the chances of AI hallucinations occurring can be greatly reduced by making the following considerations.
Provide Data Examples: Before asking the AI to generate answers to your questions, you should offer it an example of the type of data you’d like it to generate. This gives the AI an additional nudge of direction that can help it gather the correct information instead of creating falsehoods.
Offer Sources: It’s also a good idea to provide reliable sources to help the AI understand the information you seek. For example, you could ask ChatGPT to define business terms based on Investopedia.
Give AI a Role: To further guide the AI’s responses, you should aim to define its purpose within the conversation. For instance, you could give it the role of a scientist or university professor and ask it to generate responses using the same terminology as people in those job roles would.
Set Clear Parameters: Establishing boundaries and constraints is another effective means of preventing the AI from generating irrelevant or problematic information. In fact, you could even provide the AI with sources that you want it to avoid altogether.
What Does This Mean for Using AI in Business?
The effects of AI hallucinations mean that businesses using artificial intelligence, especially for research purposes, must be extra diligent in verifying the information it generates. Thankfully, AI companies are aware of the issue of AI hallucination and, in some cases, have started providing sources for generated information. Bing AI, for example, does this, and ChatGPT recently introduced a beta feature that offers users information sources.
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