The usage of AI is increasing across every sector: healthcare, entertainment, finance, and beyond. Yet, as we venture deeper into this new frontier, we must confront a significant challenge that threatens to undermine AI's potential: the phenomenon known as "AI hallucination."
AI hallucination occurs when AI systems generate outputs that are misleading, biased, or entirely fabricated, despite appearing convincingly real. This isn't merely a technical glitch; it's a fundamental issue that could lead to flawed decision-making across industries. Imagine AI-driven medical diagnoses based on hallucinated symptoms, or financial strategies built on fabricated market trends. The consequences could range from minor setbacks to life-altering mistakes.
As businesses and institutions increasingly rely on AI, addressing this issue becomes critical. We stand at a crossroads: to fully harness AI's power, we must develop robust methods to detect and mitigate hallucinations. The path forward requires a delicate balance of technological advancement and careful oversight, ensuring that our AI systems powerful, but trustworthy, too.
Let's dive deeper into hallucinations and their safe deployment through LLMs and GenAI models.
What are AI hallucinations?
AI hallucinations represent a critical challenge in the field of artificial intelligence. These phenomena occur when AI systems generate outputs that are detached from reality, lacking foundation in genuine data or discernible patterns. The implications of this issue are profound and far-reaching.
Consider a scenario where you query an AI model for factual information. Instead of providing accurate data, the system responds with unflinching confidence, delivering completely fabricated details. This is the essence of an AI hallucination - a digital mirage masquerading as truth.
The gravity of AI hallucinations cannot be overstated. As AI systems increasingly integrate into decision-making processes across industries, from healthcare to finance, the potential for these false outputs to mislead and misinform grows exponentially.
AI hallucinations vs. AI errors vs. AI biases
AI hallucinations are different from regular AI errors and biases. AI errors are simple mistakes, like misidentifying an object in an image, since AI still needs to reach the advanced stage of identification like a human.
On the other hand, AI biases come from the data it was trained on and give outputs that show societal prejudices or skewed information.
While biases can lead to harmful or misleading outputs, they are based on existing data patterns—whereas hallucinations generate entirely new incorrect data.
Common causes of AI hallucinations
By unraveling the primary catalysts of AI hallucinations, we can forge a path toward more dependable and transparent AI technologies. As we stand on the cusp of an AI-driven future, the ability to discern and address these issues will be a defining factor in the successful integration of AI across all sectors of society. Let us now shed some light on the complexities that lie at the heart of this technological challenge.
Data quality issues
When AI models are fed incomplete data, they learn incorrect patterns and give hallucinated responses. Also, if the training data lacks diversity, it can mislead AI to make wrong predictions without knowing the full picture of a situation. Even a single mislabel in a dataset can create huge errors.
For example, AI can cause a self-driving car to break suddenly in an unexpected situation, which the model wasn’t trained on. This happened in reality in May 2022, when the National Highway Traffic Safety Administration received 758 complaints about phantom braking in Tesla Model 3 and Model Y cars.
Model architecture design flaws
If AI model designs are too complex or not correctly configured, they can cause the AI to generate inaccurate responses compared to real-world data. These flaws occur in LLMs due to severe model attention mechanism issues.
If the training datasets contain biased or incomplete data, it amplifies the problem. For example, if a model is trained on data that has repeated incorrect associations, it can reinforce these associations in AI’s responses.
Studies have shown that models trained on large, diverse datasets are more prone to hallucinations due to the sheer volume of inconsistencies in the data. That’s why the right balance between data quality and architectural design is so important to ensure that AI does not hallucinate.
Insufficient training processes
Improper training techniques can create AI hallucinations. In such cases, AI fails to learn accurately and gives incorrect outputs. Technically, this occurs when models are not fine-tuned enough or when the training data lacks diversity and coverage of edge cases.
For example, a model trained on a limited dataset with gaps in specific topics or domains can overgeneralize from sparse information.
Overfitting and underfitting
AI hallucinations can also occur due to overfitting, which happens when an AI model learns too much from the training data, capturing noise along with the signal. It leads to poor generalization of new data. Similarly, underfitting happens when the model is too simple to capture the underlying patterns, giving inaccurate outputs.
The impact of AI hallucinations
Now that you know the common AI hallucinations—it’s equally important to understand they are not just wrong outputs but can be a massive problem for businesses relying on AI. Here’s why:
Misinformed decisions: If businesses rely on AI for decision-making, hallucinations can lead to incorrect investment strategies and financial losses. For example, AI can give wrong diagnoses or treatment recommendations in healthcare that could compromise patient safety and lead to severe health consequences.
Reinforcing bias: AI hallucinations can amplify existing biases in the data and give discriminatory outcomes. If an AI system is used for hiring processes, its biased nature can unfairly favor specific demographics. This will result in inequality and reduced diversity within the organization.
Reputational and legal risks: If a company publishes or acts on erroneous information, it can lose the trust of customers and stakeholders. If the misinformation harms or violates laws and regulations, it can also face legal consequences, such as lawsuits and regulatory penalties.
Cyberattacks: Threat actors can carry out cyberattacks by exploiting AI hallucinations to introduce vulnerabilities or malware in the system. They can manipulate AI systems by feeding them malicious data or tampering with the model's algorithm, causing the AI to generate false information or make incorrect decisions.
3 real-life examples of AI hallucinations
It’s clear AI hallucinations are a huge problem, and we are not the only ones saying this. There has been quite a lot of noise regarding some ridiculous hallucinations in the past. Here are our 3 top picks of when AI went so wrong that it made it into global news:
Meta AI hallucinates Trump shooting incident
During the Trump assassination attempt, Meta's AI system took to the spotlight by misinforming users about a well-documented event by claiming it was fake. This shooting event was reported and verified, but the AI dismissed it despite being explicitly programmed to avoid making statements about such sensitive topics.
The incident began when users queried Meta's AI about the shooting. Instead of providing accurate information or directing users to verified sources, it labeled the event fake news. That’s when users expressed outrage and confusion over the misinformation, questioning the reliability of Meta's AI systems.
Google's AI hallucination about cats on the moon
Did you know Google's AI summary tool suggested that astronauts had met and played with cats on the moon during the Apollo 11 mission? The AI even falsely made up quotes from Neil Armstrong, stating that he said, "One small step for man” because it is referred to as a cat's step. These statements were entirely fictional, yet the AI presented them as factual information in the summary.
This incident quickly gained attention on social media and news outlets because discussions started about the reliability of AI systems. Google AI’s credibility was also momentarily questioned as users wondered how such an error could occur in a system used by millions daily.
ChatGPT's legal fiasco
ChatGPT also landed in hot waters when it provided made-up legal citations to a New York attorney for a court case. The attorney was representing a client in an injury case, so deciding to depend on AI’s responses was a huge mistake. He submitted a GPT-written brief to the court, including citations and quotes from several supposed legal cases.
However, upon review, the judge discovered that these citations were entirely fictitious— the cases and quotes did not exist in any legal database. This led to a whole scandal in the legal community, and the attorney faced public embarrassment and professional scrutiny for his mistake.
Best practices for preventing AI hallucinations in your AI models
If you’re worried about creating a hallucinations-free AI model, check out the following best practices to mitigate AI hallucination risks in AI models:
Model training
To ensure your AI model’s response accuracy, train it on a high-quality and diverse dataset that is tested before being fed into the system. This is very challenging to do manually, so you can rely on data catalog tools like data.world to prepare your data for AI models.
The best feature of data.world’s catalog is that it uses a knowledge graph architecture to increase data accuracy. Knowledge graphs provide a structured representation of data that captures relationships and context. This structure organizes vast amounts of information in an easily accessible and interpretable way by AI models.
A recent benchmark study by data.world highlighted that LLM responses backed by a knowledge graph showed a 3x increase in accuracy across 43 business questions.
For this purpose, data.world has also launched the first AI lab in the data cataloging industry. This AI Lab aims to explore the integration of AI technologies, such as knowledge graphs and LLMs, to improve data discovery and governance.
Since knowledge graphs provide context and relevance, the AI Lab is promoting it to increase the accuracy and reliability of AI outputs. These advancements will prevent AI hallucinations by grounding AI models in real-world concepts and relationships.
Prompt engineering
AI models can hallucinate if they are provided with vague or overly broad prompts. However, prompt engineering is the art of getting the right responses from your AI model. So, companies should train their employees to design specific and unambiguous prompts that can get the desired responses from LLMs.
Here’s how you can make your prompts better:
Provide contextual information within the prompts to guide the AI towards generating accurate and relevant outputs.
Divide your prompts into three parts. First, explain what you want, then the tone you’d prefer to see the output of and provide an example to better guide the LLM.
Test the prompts iteratively and refine them based on the AI's responses.
Avoid complex sentences and jargon that might confuse the AI.
Ask AI to provide references or sources for the information it generates.
Model design/architecture
Whether you are creating a chatbot or GenAI model, you must invest in its design and architecture to achieve better outcomes. A knowledge graph can help you with this—it structures the training data to reflect real-world relationships and facts, reducing the chances of hallucinations.
Follow these best practices to make your model’s design better:
Integrate fact-checking layers because they can cross-reference AI outputs with trusted sources to verify their accuracy before presenting them.
Adopt a modular design approach to update and improve different components of AI systems independently.
Implement regular audits of the AI outputs to detect hallucinations early and implement timely corrective actions.
data.world's AI advantage: powered by a knowledge Graph
AI hallucinations are a severe issue that new and current models have to address. That’s why data.world presents an innovative knowledge graph architecture that can reduce AI hallucinations—setting a new standard for enterprise-level AI solutions.
A knowledge graph increases the accuracy and quality of your data by mapping only meaningful data based on semantics and context. It transforms rigid relational data into a flexible graph structure.
The enriched understanding allows AI systems to generate more accurate and contextually relevant answers, especially for complex business queries. So, you can also create an AI-ready data environment by using data.world’s solutions and benefit from reliable, faster time to value and increased ROI.
Book a demo of our knowledge-graph-based data catalog today and experience the power of our AI solutions.
