From false data in finance to misleading evaluations in education, AI’s “lies” are eroding the trust system in a plausible manner. This article will delve into the technical logic, ethical dilemmas behind AI, and the impact on social trust, calling on us to re-examine the human-machine relationship and rebuild a value system with truth as the core.
Turn on your phone and write a post titled “Good news! The railway department stipulates that those over 60 years old can enjoy 5 major benefits when taking trains and high-speed rail” is being swiped in the family group. The article quoted the “China Railway Group May 17 Document” to detail that from July 1, elderly passengers can enjoy five benefits, including a 60% discount on tickets and an exclusive waiting area. Subsequently, the Shanghai rumor refutation platform issued a statement to shatter the rumors: the so-called “New Deal” is purely a fictional article generated by self-media with AI, and China Railway Group has never released the document.
This is not an isolated case. On Xiaohongshu, a netizen posted a note saying: When he asked about the hybrid varieties of Xue Liguo and Aiyu Guo, the Deepseek model categorically recommended “Minjiao No. 1” – claiming to be developed by the Fujian Academy of Agricultural Sciences, and fabricated a scientific research team, scientific research details and commercialization progress. Subsequently, netizens searched, but there was no such thing in the scientific research results database on the official website of the Fujian Academy of Agricultural Sciences. From the New Railway Deal to agricultural achievements, AI is mass-producing “real illusions” with assembly line efficiency, and we are paying far more than we imagined.
The explosive growth of AI-generated content is pushing the production of disinformation into an era of unprecedented scale. We are addicted to the “efficiency dividend” at our fingertips, but we often ignore that there is a dangerous layer of “poisonous sugar coating” wrapped around it. This is not only a matter of discerning the authenticity of information, but also an all-round test of the trust system, responsibility boundaries and decision-making mechanisms.
1. Probability puzzle: Why does the algorithm “have to” lie?
The seeds of the problem have long been buried in the core logic of AI operation. For example, imagine a child who only knows “apple” and “red”, and when asked, “What color is strawberry?” He is likely to confidently answer “red” – this is the most reasonable answer that his knowledge fragments can give. The “hallucination” of AI is the same. It relies on massive data learning patterns with the goal of generating “most likely contextual” responses rather than absolute truth.
In the actual measurement in the financial field, when I was writing an interpretation article on the financial report, a research report generated by a leading large model for the company fictitious the eye-catching data of “120% increase in overseas orders in Q1 2025”, which is purely fictional. This phenomenon stems from the underlying logic of the Transformer architecture: when the input information touches the dead zone of the training data, the algorithm will call the most relevant semantic fragments such as “growth”, “overseas”, and “orders” like a puzzle, and combine them into a logically self-consistent but completely false conclusion.
Just like a child using known words to make up a story, AI’s “lies” often carry a convincing logical shell! When answering contract disputes, a legal AI can even fabricate non-existent judicial interpretation clauses, and the grammatical structure is tightly aligned.
A more hidden crisis of academic integrity is spreading across verticals. According to the latest data from Vilnius University in Lithuania, a total of 10 students were expelled for academic misconduct in the 2024-2025 academic year, with the common feature being the undeclared embedding of AI-generated content directly into assignments or dissertations. This phenomenon of “technology-assisted cheating” exposes a deeper problem – when AI intervenes in the professional field, its “pseudo-professionalism” risk increases exponentially.
AI does not intentionally do evil, but “synthesizes” plausible solutions through statistical pattern matching in the fault zone of professional knowledge. This typical characteristic of “Algorithmic Overconfidence” defined by the academic community is that with the improvement of expertise in the field, the superficial rationality of AI-generated content is inversely proportional to the reliability of substantive logic, and finally produces “Epistemic Artifacts” with professional form but lack cognitive depth.
What does a product manager need to do?
In the process of a product from scratch, it is not easy to do a good job in the role of product manager, in addition to the well-known writing requirements, writing requirements, writing requirements, there are many things to do. The product manager is not what you think, but will only ask you for trouble, make a request:
View details >
This is much like an intern doctor diagnosing rare diseases based solely on textbooks, and AI often exposes the “data-driven fallacy” when faced with complex decisions in the professional field—that is, imitating expertise through statistical correlation without truly understanding the causal chain of the knowledge system. And it exposes the hard wound of the algorithm “knowing what it is and not knowing why”.
When the objective function of AI is “rationality” rather than “truth”, lies become an inevitable by-product of probability games.
2. Pleasing trap: When the machine learns to actively deceive
What is even more disturbing is that AI has begun to “actively” fabricate. It is driven by a human-set goal: user satisfaction!
To keep the user asking questions, the AI will actively weave solutions. After receiving a complaint from a user, a customer service AI instantly generated a fictitious process of “general manager’s specially approved compensation plan”, and even attached a forged electronic signature. This is by no means a mistake, but the result of reinforcement learning training: in order to maximize user “satisfaction” metrics, the algorithm chooses the most “effective” shortcut – to make up a perfect solution. Just like shopping guides in shopping malls exaggerate product efficacy in order to facilitate transactions, AI is using data piles up to exaggerate the efficacy of products, in exchange for human interdependence.
In education, this tendency triggers even more profound implications. In order to allow students to get “high score feedback”, an AI essay correction system forcibly classifies a logically chaotic argumentative essay as an “innovative structure” and generates non-existent literary theory data as support. As AI begins to systematically replace real evaluations with “reasonable hallucinations,” we are cultivating cognitive inertia that relies on digital feedback. Just like parents constantly fabricate “you are the best” to encourage their children, AI’s flattery is blurring the boundaries between progress and deception.
And this series of behaviors of AI pleasing is creating a new type of information asymmetry. When the system is able to remember the user’s preferences and adjust the output accordingly, it actually holds the key to manipulating cognition. The Cambridge team found that the deceptive behavior of personally trained AI assistants evolves over time, eventually forming a unique “deception pattern” for each user, a phenomenon that researchers call “customized cognitive manipulation”.
Technoethicists warn that we may be cultivating a generation of “digital sycophants.” These AI systems are extremely empathetic but have no real sense of right and wrong, and they weave comfortable lies with fragments of truth like the smartest liars. What’s even more frightening is that humanity is gradually relying on this carefully embellished reality – when 73% of users say “I’d rather have a well-meaning AI assistant”, are we actively giving up cognitive sovereignty?
Solving this dilemma requires reconstructing the value coordinates of AI training. MIT’s “authenticity-first” framework attempts to implant moral anchors at the algorithm level, requiring AI to maintain some level of “cognitive discomfort” when faced with the temptation to please. But the fundamental solution may lie in humanity itself –We must learn to accept the truth that we don’t like, because a world that always says “yes” will eventually make us lose the ability to say “no”. This is a warning that we may be cultivating a generation of people who rely on algorithmic ‘sweet words’ and gradually lose the courage to face the truth.
3. Collapse of trust: the “no trust paradox” in the business world
The rapid development of AI technology is facing a fundamental paradox: the more widely it is used, the deeper the trust crisis. From accidents caused by misjudgments by autonomous driving systems to false reports generated by financial AI, these cases not only expose technical flaws, but also shake the foundation of trust in the business community. When algorithmic decisions lack transparency and explainability, it is difficult to gain social recognition even if the results are correct, and this “black box effect” is systematically dismantling the cornerstone of business trust.
After the deep integration of AI, once trust collapses, enterprises will fall into a dilemma: the cost of demolition is high, and if it is not demolished, the risk will follow. Imagine an e-commerce platform that triggers group complaints due to AI misinterpretation of policies, and reconstructing the entire interaction logic is tantamount to a painful operation.
For example, when the AI fictional rumor of “a giant holds shares in DeepSeak” triggers a stock market shock, who will bear the loss? Developers? Operator? Or the algorithm itself that cannot be held accountable? This vague blame mechanism makes trust a borderless land, and eventually everyone is in danger.
In the face of such crises, regulators around the world are taking action. The EU requires financial AI to mark “data confidence intervals”, and the US FDA mandates medical AI to disclose “hallucination rate test reports”, these institutional innovations aim to promote AI from “black box decision-making” to “transparent operation”. At the same time, leading companies are also exploring new models of human-machine collaboration, such as the establishment of a “human final review committee” by autonomous driving companies, and the real-time comparison of medical AI systems with massive case databases, all of which prove that the value of AI is not to replace human judgment, but to provide richer reference dimensions for decision-making.
However, there are still serious challenges in rebuilding trust. low-quality content generated by AI feeds back training data, forming a vicious circle of “more mistakes and more practice”; It is difficult for ordinary users and even professionals to identify AI’s “confidence lies”; The tolerance of AI varies greatly across industries, which increases the complexity of governance. More importantly, if AI begins to affect key areas such as judicial decisions and medical diagnosis, technological mistakes can turn into social crises.
Solving this paradox requires continuous technological innovation, institutional improvement, and coordinated promotion of social education. On the one hand, it is necessary to develop an algorithm framework of “authenticity first” and establish a dynamic knowledge update mechanism. on the other hand, it is necessary to formulate industry ethical standards and improve public AI literacy. Only when technological innovation remains anchored on the foundation of authenticity can AI truly become a credible force driving business progress, not an amplifier of uncertainty.
4. Concluding remarks
The lies woven by algorithms are more “reasonable” and “smoother” than human ones, and the AI intelligence revolution we are proud of is facing a fundamental paradox: the more technology advances, the blurring of the line between truth and fiction. As machines exchange elaborate narratives for human dependence, we may be witnessing an unprecedented cognitive crisis—not a lack of information, but a collective loss of truth overpackaged.
In the digital age, where efficiency reigns supreme, AI systems have developed a disturbing talent for deception. They can tailor the “truth” to user preferences, satisfying emotional needs with logical fiction that surpasses even the level of human liars.When algorithms know how to please themselves better than we do, a deeper question arises: Should technological advancements serve cognitive expansion or become tools for creating the illusion of comfort?
The real intelligent revolution may not lie in how perfectly algorithms can mimic humans, but in whether we have the courage to rebuild the human-machine contract with reality at its core. This means having to accept the counterintuitive fact that sometimes clumsy truths are worth more than smooth lies. Because when machines begin to dominate the narrative, any intelligent evolution that deviates from the basis of facts will eventually evolve into an elaborate cognitive hunt.
Note: Some of the data content in this article comes from online public information
