Be careful who you trust because artificial intelligence misinterprets reality

When AI misreads reality: What EBU report reveals about AI errors in news analysis

Artificial intelligence is increasingly present in our lives — from how we get information to the economic and political decisions we make. But a new European report draws attention to a sensitive issue: even the most advanced AI models can misinterpret public events, news or official statements, thus influencing the collective perception of reality.

The effects of these errors do not stop at the technical level. They are amplified in the chain, especially as more and more publications and content creators use artificial intelligence to generate texts automatically, without a real editing or verification process. In the rush for traffic and quick monetization, content becomes a mass product — produced quickly, but rarely read even by those who publish it.

Many online publishers rely on AI models to publish dozens of articles daily, betting on distribution algorithms and automated advertising revenues. The problem is that these texts, even if they seem coherent, can contain misinterpretations, truncated statements or superficial analysis. When such an error is taken over and republished by other sites, it quickly becomes an apparently verified “fact” that circulates unchecked on social networks.

In this context, AI is no longer just a working tool, but a multiplier of human error, and the absence of an editorial filter turns false information into a profitable commodity. The consequences are direct: readers are becoming increasingly confused, trust in online media is declining, and the quality of public debate is being affected. In essence, algorithms that are supposed to save us time end up consuming our judgment.

What the European Broadcasting Union (EBU) report says

Published in October 2025, the European Broadcasting Union (EBU) report — conducted in collaboration with the BBC and other public media services in Europe — analyzed how AI systems process and deliver news information.

The main conclusion of the study is worrying:

“AI is systematically distorting news content, regardless of language or territory.”

The EBU report shows that many AI systems are designed to provide clear and concise answers — but in the process of simplification, they often lose the nuances that give a news story its full meaning. For example, when a political statement contains a conditional (“if,” “in certain situations”), the AI ​​model may omit it to make the text stylistically “cleaner.” The result is a seemingly safe sentence but fundamentally different in meaning.

This tendency to “round off” information occurs because AI models do not think contextually. They analyze texts according to word frequency and linguistic patterns in the datasets they were trained on, not according to the author’s intention or political context. This results in summaries that turn a diplomatic nuance into a categorical statement or an economic specification into a “promise.”

AI also tends to standardize tone and adapt it to the dominant style of the data it was trained on. If most of the texts in the training set express indignation or urgency, the system will reproduce the same tone even in the case of a neutral news story. In this way, a simple report about a conference can become, in the AI ​​version, a “controversy” or an “alarm signal”.

Another phenomenon observed by the EBU researchers is the unintentional omission of essential details. AI models tend to eliminate figures, dates or historical references considered “unimportant” but which are in fact vital for understanding the context. Thus, an economic analysis can seem alarming simply because the system ignored a paragraph explaining the temporary nature of a decline.

Overall, the report warns that these errors are not rare exceptions, but a structural feature of the way AI “learns” to summarize the world. Even if the technological intention is positive — clarity and efficiency — the social effect can be the opposite: a reality simplified to the point of distortion. And when millions of people consume such “processed” versions of events every day, truth becomes an increasingly relative product.

According to the authors of the EBU report, AI errors arise not only because of the technical limitations of the algorithms, but also from the fundamental structure of how these models are trained. Artificial intelligence models analyze enormous amounts of text taken from the internet or from publications, and this text already contains opinions, prejudices and subjective interpretations. Thus, the systems learn to reproduce these patterns without having the ability to critically evaluate them.

This structural problem means that any bias or misinformation present in the training data can be amplified when the AI ​​generates summaries or explanations. Even a very advanced model can turn a simple speculation into a near-certain statement or completely omit details that contradict the dominant narrative.

Furthermore, the lack of a human editorial filter in the publishing process means that these errors quickly reach the end readers. Without verification and correction, mistakes can spread virally, especially in the online environment, where automatically generated articles are redistributed by other platforms and news aggregators.

The authors point out that the combined effect of data bias and the lack of editorial control can have significant social consequences: the public can be misled, and trust in the media decreases. In addition, subtle errors, difficult to detect, are the most dangerous, because they can be taken as “facts” in public discussions and in individual or political decisions.

The report emphasizes that the problem of AI is not just a technical issue of optimizing algorithms, but a structural challenge related to data quality, transparency and editorial responsibility. In the absence of verification and correction measures, AI can unintentionally amplify disinformation, affecting both public perception and the quality of democratic debate.

The EBU report does not condemn artificial intelligence; on the contrary, it provides a clear and realistic picture of the current state of these technologies. The authors emphasize that AI models do not “understand” information in the same way that humans do. They do not have consciousness, intention or the ability to evaluate contextual meanings; their main function is to recognize and reproduce patterns in the data they have processed.

This lack of contextual understanding can generate surprising and sometimes erroneous results. For example, AI can interpret irony literally, exaggerate a tone or remove essential details because it does not “feel” their relevance in a news story. Without adequate context, what should be a neutral story can be transformed into a distorted or even alarming interpretation. Furthermore, the report emphasizes that the quality of the training data is crucial. Incomplete, unbalanced or biased data can lead to erroneous conclusions, even if the AI ​​model is technically capable. Thus, errors are not always “accidents” but a result of how AI “learns” from an imperfect information universe.

Another important point highlighted by the report is that AI errors do not only affect technicians or researchers, but also the general public. Wrong analyses or summaries, if taken up and redistributed, can influence public opinion, the perception of political or social events and trust in the media. Without a human filter for verification, these errors spread quickly.

The EBU report sends a balanced message: artificial intelligence is an extremely powerful and useful tool, but it cannot be considered a guarantor of truth. AI models can process huge volumes of information and quickly generate summaries or analyses, but they are no substitute for human judgment and our ability to correctly interpret events.

Even the most advanced models do not have the ability to assess the importance of context or understand the subtle implications of a political or social statement. They simply identify patterns in data and generate results based on them. Thus, even when producing coherent and compelling texts, AI can make mistakes in ways that are difficult to detect without the intervention of an editor or critical reader.

Understanding the limitations of these systems becomes essential. The public and content creators must be aware that errors are not rare accidents, but possible consequences of the way AI learns from imperfect data. Only by being aware of these limitations can we avoid the spread of misinformation and misinterpretations of events.

Fact-checking remains the fundamental responsibility of humans. Readers must confront AI-generated results with original and multiple sources, and editors must include an editorial control filter before any automated content is published. Without this step, AI’s power to rapidly generate content can become a source of confusion or misinformation.

The EBU report reminds us that AI is a valuable partner, not an infallible judge. The power and speed of technology must be accompanied by responsibility, discernment and media literacy, so that information remains accurate, complete and useful to society.

It is an essential reminder for all those who use AI assistants, chatbots or other automated content generation tools: these systems are not news sources, but only intermediaries that process and render information. They do not independently verify facts and do not have the ability to analyze the social, political or cultural context in which events occur.

Even if AI-generated texts appear coherent and convincing, they can contain subtle mistakes or distortions that alter the original meaning of the news. This subtlety is what makes AI errors often harder to detect than obvious human errors. Without verification, readers may take as truth what is merely an incomplete or incorrect interpretation.

The EBU report highlights that user responsibility is becoming crucial. AI can be a valuable tool for summaries, translations or content suggestions, but the final decision on the accuracy and relevance of information must remain in the hands of humans. This requires discernment, source verification and a minimum knowledge of the context.

Furthermore, interaction with AI should not replace critical thinking. Even if a system provides a quick and seemingly complete answer, the user must ask themselves: “How does the AI ​​know this? What is the source? What details might I be missing?” This process of critical evaluation turns AI into a useful partner, without confusing it with an infallible authority.

The central message is clear: AI is not a journalist, editor or arbiter of truth. It is a powerful tool, but with limits, and awareness of these limits and verification of information remain the fundamental task of everyone who uses such technologies.

At the same time, the report opens the discussion about the responsibility of companies that develop AI models: who is responsible for distorting information? How can verification and transparency mechanisms be implemented without limiting innovation?

Tips for laypeople: how to protect yourself from flawed AI analyses

In a world where technology is evolving faster than our ability to understand it, personal responsibility becomes an essential filter for information consumption. Even though AI can quickly process huge amounts of data and generate summaries or analyses, the decision to accept or verify this information remains in our hands.

This responsibility involves more than just critical reading. It involves checking sources, comparing information from multiple perspectives, and being aware of the limitations of automated systems. Without this active involvement, people risk being led by erroneous or partially interpreted results generated by AI, turning the tool into a source of confusion.

The EBU report emphasizes that technology, no matter how advanced, cannot replace human judgment. The fact that an AI model quickly generates text does not mean that that text is correct or complete. Therefore, personal responsibility becomes a “safety filter” that protects both the quality of the information we consume and the decisions we make based on it.

Moreover, personal responsibility does not only concern consumers, but also those who create and distribute content. Editors, bloggers or creators of automated materials must verify the accuracy of information generated by AI before publication, in order to prevent the spread of errors and misinformation.

In an era where the speed of technology can outpace the speed of critical thinking, individual responsibility and active involvement remain the most effective tools for protecting the truth. Only through a conscious approach can we turn AI into an ally, not a source of confusion.

Some basic tips:

• Check the source of the information. If an analysis seems suspicious or too “confident” in itself, look for the original article or an official statement.
• Compare multiple sources. AIs can interpret the same event differently; read other publications for a complete picture.
• Pay attention to tone. AI-generated text that exaggerates emotions (enthusiasm, panic, outrage) is often a sign that the interpretation is not neutral.
• Use AI as a tool, not a judge. Let it help you understand, but don’t let it decide what’s true.
• Media education matters. The better we understand how these systems work, the harder we are to be manipulated by their errors.

Be curious, but don’t be gullible. Check, ask, compare — and the digital world will become a safer place for everyone.

The return of personal blogs: information filtered by humans

There is a real possibility that, in the near future, we will witness a return of personal blogs and independent platforms, where information is filtered and interpreted by a human operator. As AI errors and disinformation become more visible, readers will seek out sources where human judgment and discernment remain at the center of the information process. These blogs can provide not only news, but also context, nuance, and reasoned opinions that are difficult for algorithms to replicate.

Such a return could transform the way we consume content online. Instead of receiving only automated summaries or articles optimized for traffic, readers will be able to choose blogs where the editor checks, selects, and explains the information, providing clear and well-founded interpretations. This type of personalized and verified content can rebuild lost trust in automated media and AI-generated news feeds.