AI Hallucinations

Generative artificial intelligence (AI) has become widely popular, but its adoption by businesses comes with a degree of ethical risk. Organizations must prioritize the responsible use of generative AI by ensuring it is accurate, safe, honest, empowering, and sustainable.

What is AI Hallucinations?

An AI hallucination occurs when an artificial intelligence model produces inaccurate information or misleading results but conveys it as if it were factual.

Examples of AI hallucinations

AI hallucinations can take many different forms. Some common examples include:

AI hallucinations can manifest in various ways, with several typical examples being:

• Incorrect predictions: An AI model may predict that an event will occur when it is unlikely to happen. Example: an AI model that is used to predict the weather may predict that it will rain tomorrow when there is no rain in the forecast.
• False positives: When working with an AI model, it may identify something as being a threat when it is not. Example, an AI model that is used to detect fraud may flag a transaction as fraudulent when it is not.
• False negatives: An AI model may fail to identify something as being a threat when it is. Example: an AI model that is used to detect cancer may fail to identify a cancerous tumor.

To many users, generative AI’s hallucinations are an irritating bug that they assume the tech companies will fix one day, just like email spam. To what extent companies can do so is currently a subject of active research and fierce contention. Some researchers argue that hallucinations are inherent to the technology itself. Generative AI models are probabilistic machines trained to give the most statistically likely response. It is hard to code in human traits such as common sense, context, nuance or reasoning.

Dangers of AI Hallucination.

For example, the risk of harm when an generative AI chatbot gives incorrect steps for cooking a recipe is much lower than when giving a field service worker instructions for repairing a piece of heavy machinery. If not designed and deployed with clear ethical guidelines

Generative AI systems can produce inaccurate and biased content for several reasons:

1. Training Data Sources: Generative AI models are trained on vast amounts of internet data. This data, while rich in information, contains both accurate and inaccurate content, as well as societal and cultural biases. Since these models mimic patterns in their training data without discerning truth, they can reproduce any falsehoods or biases present in that data (Weise & Metz, 2023).
2. Limitations of Generative Models: Generative AI models function like advanced autocomplete tools: They’re designed to predict the next word or sequence based on observed patterns. Their goal is to generate plausible content, not to verify its truth. That means any accuracy in their outputs is often coincidental. As a result, they might produce content that sounds plausible but is inaccurate (O’Brien, 2023).
3. Inherent Challenges in AI Design: The technology behind generative AI tools isn’t designed to differentiate between what’s true and what’s not true. Even if generative AI models were trained solely on accurate data, their generative nature would mean they could still produce new, potentially inaccurate content by combining patterns in unexpected ways (Weise & Metz, 2023).

The Bottom Line

Generative AI tools aren’t always capable of understanding emotional or business context, or knowing when they’re wrong or damaging.

Humans need to be involved to review outputs for accuracy, suss out bias, and ensure models are operating as intended. More broadly, generative AI should be seen as a way to augment human capabilities and empower communities, not replace or displace them.

References

https://mitsloanedtech.mit.edu/ai/basics/addressing-ai-hallucinations-and-bias/