Simone Raponi : 12 November 2025 22:06
Hallucinating, for a human, means perceiving things that aren’t actually present in our environment. When we talk about “hallucinations” in artificial intelligence (AI), we’re referring to a situation in which an AI model “sees” or “interprets” something that isn’t actually present in the data . But how can a machine “hallucinate”? Let’s find out.
An artificial intelligence model, like a neural network, somewhat emulates the functioning of neurons in the human brain. These models learn from large volumes of data and, through this learning, develop the ability to perform tasks such as recognizing images, interpreting language, and much more.
An AI model learns through a process called “training.” During training, the model is exposed to a large number of examples, and through iterations and adjustments , it “learns” to perform a given task.
In artificial intelligence, “hallucinations” refer to misinterpretations or distorted perceptions a model can have when processing certain information. These errors can be both fascinating and disturbing, depending on the context in which the model is used. But how and why do these hallucinations occur?
Overfitting is a major cause of hallucinations in AI models. It occurs when a model learns too well from its training data , to the point of also capturing the “noise” present in it. Imagine having a model trained to recognize cats based on photographs. If the model suffers from overfitting, it might recognize specific scratches or blurs in the photos in the training set as distinguishing features of cats. Consequently, when presented with a new photo with similar scratches or blurs, it might mistakenly identify it as a photo of a cat.
AI models are only as useful and accurate as the data they’re trained on. There’s a famous saying in AI, ” Garbage In, Garbage Out.” If the model receives garbage as input (bad data), it will produce garbage as output (bad inferences). If the data is unbalanced or unrepresentative , hallucinations can become frequent. If we train a model to recognize fruit and feed it many images of apples and only a few of bananas, it might tend to misclassify a banana as an apple if the lighting conditions or angle make it slightly resemble an apple.
Artifacts in the data can create distortions that the model might interpret as relevant features. If a model is trained on images of dogs that frequently feature a certain type of collar, it might begin to recognize the collar as a distinctive feature of the dogs, leading to hallucinations when it encounters that collar in different contexts.
Extremely complex models, such as some deep neural networks, can have millions of parameters . The complexity of such models can make it difficult to understand how they make decisions, leading to unexpected or unintended interpretations of the data.
Sometimes, AI models can enter a feedback loop where their decisions or predictions influence the data they are subsequently trained on. Suppose we have an AI model used on social media to suggest content to users. If it starts incorrectly showing a certain type of content to a user and the user interacts with it, the model might interpret this interaction as confirmation that the user is interested in that content, further reinforcing the error .
Hallucinations in artificial intelligence may seem, at first glance, merely technical curiosities or minor errors in an otherwise impressive system. However, these deviations from precision have profound implications , both in the world of technology and in broader sectors of society. Let’s examine some of the key consequences.
Hallucinations can create vulnerabilities in critical systems . For example, if a facial recognition system “hallucinates” and confuses one individual with another, this could lead to false identifications in security contexts.
Example : Surveillance systems at airports or other sensitive locations may grant or deny access to the wrong people due to such errors.
In the medical field, misinterpreting data can have serious consequences. If an AI system used to analyze medical images “hallucinates” a nonexistent tumor, it could lead to misdiagnosis and inappropriate treatment .
Hallucinations could lead to incorrect legal decisions. For example, AI systems used to assess a criminal’s likelihood of recidivism could produce assessments based on faulty data or implicit biases, unfairly influencing judicial decisions.
In the financial world, where trading algorithms operate on a millisecond scale, hallucinations can cause huge financial losses in a very short time. A distorted perception of the market could lead to incorrect investment decisions .
If a virtual assistant or robot “hallucinates” in response to human commands, it could reduce user trust in the system . This erosion of trust can limit the adoption and effective use of AI technology in everyday life.
If a model hallucinates based on unbalanced or biased training data, it can magnify and perpetuate existing stereotypes and prejudices , leading to unfair decisions and actions in a variety of areas, from advertising to hiring decisions.
One of the most intriguing, yet often overlooked, aspects of AI technologies is “hallucinations.” These distorted patterns or misinterpretations by models aren’t just random phenomena: they’re manifestations of the inherent challenges of training and implementing AI.
First, it’s important to understand that every AI model is a product of the data it was trained on. Imperfections or distortions in this data can easily lead to hallucinations. This reminds us of the importance of carefully selecting, preparing, and verifying data before training any model.
Furthermore, hallucinations are not simply anomalies; they have real implications across a variety of fields. From security to medicine, from ethics to finance, AI errors can have tangible and often serious consequences. This underscores the need for appropriate oversight , rigorous testing , and ongoing verification of the use of AI models, especially in critical contexts.
Furthermore, AI hallucinations raise profound questions about our relationship with technology. Trust is essential when integrating automated systems into our lives. If an AI makes repeated or unexpected mistakes, our trust can be undermined, making the adoption and acceptance of such technologies more difficult.
Ultimately, these hallucinations highlight the need for greater transparency and interpretability in AI. Understanding how and why a model makes certain decisions is crucial , not only to correct errors but also to ensure that AI technologies operate ethically and responsibly.
In short, while AI hallucinations offer a fascinating glimpse into the technical challenges of training and deploying models, they also serve as a powerful reminder of the ethical, social, and practical responsibilities that accompany the use of artificial intelligence in the real world. As we advance in building and integrating AI, it is essential to maintain a holistic view, considering not only what the technology can do, but also what it should do.
