Is AI truly impartial, or does it reflect—and sometimes amplify—the limitations of our own way of thinking? In the ongoing debate about the impact of AI on everyday life and the world of work, concepts such as bias and hallucinations take center stage.
Although they are different phenomena by nature, they share a common origin: the statistical and predictive structure underlying modern artificial intelligence systems.
In this article, we explore the nature of these two phenomena, which reflect two complementary sides of the same technology: artificial intelligence that does not know but calculates, does not understand but predicts.
When AI inherits our biases
A bias is a prejudice or distortion that is incorporated into AI models through the data used to train them. Artificial intelligence does not invent, but learns from the data it receives. If this data is incomplete, unbalanced, or reflects historical and cultural biases, the result will be a model that replicates those same imbalances.
Consider a personnel selection system based on CV analysis: if historical data shows predominantly male hires, the model risks automatically penalizing female candidates.
Biases can emerge in various contexts:
- recruiting, with indirect discrimination against ethnic or gender groups;
- facial recognition, often less accurate on non-Caucasian faces;
- language models, which tend to perpetuate cultural or sexist stereotypes.
As the Harvard Business Review also points out, an AI system is conditioned by the quality of the data on which it is based. This is why more careful design with inclusive datasets, model audits, and continuous verification of results are essential tools for reducing the impact of bias and making AI more equitable.
AI that seems to know, but actually invents
Have you ever asked a virtual assistant a question and received a completely wrong answer, but one that was given with absolute confidence? Well, you have probably witnessed a case of hallucination.
A hallucination occurs when a model generates false information, but expresses it in a credible way. It does not do this to deceive: the AI simply does not know it is wrong. It has no knowledge of the real world, but merely predicts the most likely sequence of words based on the data it has been trained on. This is how it can “imagine” dates, quotes, bibliographic references, names, or facts that do not exist but sound perfectly plausible.
A generative model can, for example, suggest invented medical treatments, confuse legal judgments, or write articles full of unrealistic but stylistically impeccable data. The confidence of the tone is, in fact, one of the most insidious aspects of hallucinations: the message appears reliable even when it is completely false.
This is why it is important to verify information generated by AI, especially when used in professional or public contexts.
Two sides of the same AI coin
At first glance, bias and hallucinations seem like distinct phenomena: the former are inherited from the data, while the latter emerge when AI generates content. Yet both share a fundamental element: they derive from the predictive nature of artificial intelligence.
Generative models do not have a real understanding of the world, but merely calculate the most probable response, not the most correct one. If the source data is distorted, biases appear; if the information is incomplete, AI fills in the gaps by inventing. In both cases, the user risks receiving incorrect but credible results without noticing the problem.
The danger increases when bias and hallucinations intertwine. An example? A chatbot trained on partial texts could ‘imagine’ false data that is perfectly consistent with a stereotype and present it as true. Or a model used in healthcare could suggest a non-existent cure, influenced by gender or ethnic biases present in the data.
That’s why bias and hallucinations should not be considered mere errors, but structural characteristics of AI. Only by recognizing them can we develop more reliable systems and, at the same time, educate more aware users.
Reducing bias and hallucinations: what AI developers (and users) can do
The limitations of artificial intelligence are not inevitable, but they require careful design and conscious use. Addressing bias and hallucinations means taking action on multiple levels, from data quality to the way AI is used in the real world.
CURATING DATASETS: MORE DIVERSITY, LESS BIAS
Most bias stems from incomplete or unbalanced data. To reduce it, it is essential to build representative datasets that include different perspectives in terms of gender, ethnicity, culture, and socioeconomic context. Data inclusivity is the first step toward more equitable AI.
MODEL AUDITING AND TRANSPARENCY
Explainable AI allows us to understand how a model makes decisions. Regular technical audits and impact assessments help to identify and correct biases, ensuring greater transparency and accountability.
HUMAN SUPERVISION AND CONTINUOUS VERIFICATION
No model should operate without human oversight, especially in critical contexts such as healthcare, justice, finance, or public communication. Verifying AI responses is not optional, but a necessary condition.
FACT-CHECKING TOOLS AND ANTI-HALLUCINATION FILTERS
Technical solutions such as cross-checking systems or automatic fact-checking help identify fabricated content before it is used. The retrieval-augmented generation approach, which combines models with reliable databases, is also showing promising results.
The role of the user: recognizing the limits of AI
Users play a crucial role in preventing the effects of bias and hallucinations. AI is not a source of absolute truth, but a tool to be used critically and consciously.
Every response generated must be interpreted carefully, especially when it is used for decisions or public content. Verifying sources, comparing information, and recognizing warning signs—such as citations that do not exist or overly generic data—are essential practices.
At Olidata, we believe in responsible, verifiable AI that serves people and combines technological innovation with human awareness. Only in this way can AI become a reliable ally, even in the most complex contexts.