With the rise of generative models such as ChatGPT and DeepSeek, artificial intelligence writing has widely penetrated into academic and news fields, sparking high attention to the authenticity of texts.AI detection platforms such as GPTZero and Turnitin mainly rely on statistical features such as vocabulary complexity and grammatical structure to determine the source of text.However, in real-world applications, its accuracy is far from ideal: concise and clear human expressions are often misjudged, while optimized AI text can be easily avoided.This' cat and mouse game 'is trapped in an infinite cycle of technological confrontation.

Struggling to break through the mud of the AI information age, deeper ethical paradoxes emerge: how do misjudgments prove themselves innocent?Check if it violates privacy?These issues reflect the fundamental tension between technological determinism and humanistic values.Just as the principle of prudence and tolerance emphasized in the judiciary's "in dubio pro reo" emphasizes, the academic field can also introduce the judgment criteria of "in dubio pro humano".When it is difficult to distinguish between truth and falsehood, priority should be given to respecting the possibility of human writing.After all, technology determines speed, while diversity of ideas determines direction and breadth.

Since Alan Turing proposed the Turing Test in his paper in 1950, artificial intelligence was introduced by scientists such as John McCarthy, Mavins, and BenQ at the Dartmouth Conference in 1956. In 1997 and 2016, Deep Blue defeated the chess champion and Alpha Go defeated the Go champion, demonstrating the powerful learning ability of AI. With the development of technology, in 2022, Chat GPT was released, and artificial intelligence officially entered the public and caused a global impact.But with the widespread use of AI, some people will use AI to complete certain specific tasks, such as assignments, papers, etc. People usually use AI to detect the AI rate of articles made with AI.

The core principle of using AI detection tools to determine AI classes is to use machine learning models to identify human style and AI generated text style for segmentation, such as extracting a large number of quantifiable features from text, including the predictability of vocabulary;Confusion level (human writing has a higher level of confusion with standard language models, while AI generated ones are more familiar with standard patterns, resulting in lower levels of confusion in AI writing);Watermark technology (some AI model providers, such as OpenAI, may embed invisible, statistically significant "watermark" patterns in the generated text).This requires the model to deliberately introduce some detectable signal that deviates slightly from the statistical rules of natural language during the generation process.）Wait.Its output is a probability estimate, also known as the AI rate, representing the likelihood of text being generated by AI.

Based on such a rigorous detection process, the success rate of AI detection has achieved great success. Whether in the field of teaching (such as Columbia University using chat GPT zero to randomly check 3000 final papers and detecting 40% generated by AI, with an accuracy rate of 89% confirmed by manual verification), media publishing (science fiction magazine "Clarkesworld" AI submissions forced to close the submission system, with a monthly AI submission volume of up to 500+), or technical confrontation cases (Open AI assisted the US government in tracking more than 5000 watermarked Russia Ukraine wars).AI fake news is of great help.

However, due to the limitations of current technology and adversarial measures (humans can polish and rewrite AI generated text by adding specific style change errors, thus bypassing detection and abbreviated as humanization processing), false positives (false positives: mistaking human original text as AI generated) exist.False negative: Failure to detect genuine AI generated text. Can AI programs written by humans determine the authenticity of articles?

In 2024, the University of Oxford in the UK conducted a study on this issue titled "The Force Positive Problem in AI Text Detection". Professors and researchers tested seven mainstream AI detection tools, including Turnitin GPT zero, and found that non-native English speakers had a writing error rate of up to 38% (simply because non-native speakers often use "template sets" to assist learning in foreign languages, which are judged as AI generated due to their standardized grammar and complex vocabulary). The error rate of academic abstracts, technical reports, and other texts was as high as 25% (simply because of their rigorous structure, similar to AI generation mode). Through this study, I realized that AI detection has unreliability. There is a need for manual detection, as AIIt is impossible to recognize the core characteristics of human beings such as creative intention, culture, and context, which leads to the classification of normativity in human creation as AI characteristics. Moreover, is it an ethical loss for universities to compromise academic integrity to AI?

In reality, many conflicts have arisen due to AI detection algorithms. For example, in 2023, several students at Vanderbilt University in the United States were marked as 100% AI generated by the turning AI detection function for using chat GPT to assist writing. However, the students insisted that their works were original. Finally, after repeated checks by the professor, it was found that the reason for the misjudgment of the student's text was due to the concise academic writing style (such as passive voice, structured sentence structure), which triggered the AI detection algorithm (the program believes that human writing is highly chaotic because sentence jumping is strong, and AI generated chaos is low because the sentence can be predicted, while the passive voice and sentence structure in multiple places are highly standardized and detected by AI as vocabulary with high predictability and confusion). Low, identified as AIFinally, the school suspended the punishment of the students and issued a statement reminding teachers that AI test results cannot be used as sole evidence.

Whether it is research on vulnerabilities or conflicts in reality, it indicates that there are vulnerabilities in AI detection, not only in detection and anti detection, but also in the mismatch of rights and responsibilities caused by the transition of judgment power to AI, and whether using a unified algorithm for AI detection can solidify the diversity of human thinking?I will carefully consider it in the following chapters.

AI detection systems are often presented as neutral tools - objective, consistent, and unbiased.However, the judgment indicators they rely on, such as sentence complexity, language predictability, and vocabulary diversity, are actually based on a set of assumptions about what constitutes normal human writing.This set of standards is not universally applicable.Non-native writers, students with learning differences, or those who adopt non-traditional expression styles are often misjudged - not because they are dishonest, but because they are statistically 'unsociable'.Most non-native speakers often use the method of "memorizing templates" to assist in learning a new language, and they often pay special attention to the standard grammar of the language when learning. In order to learn this language, they often use various advanced sentence structures and rhetorical devicesOn the internet and social media, it is common to see native English speakers jokingly calling their students Shakespeare

Therefore, this so-called "neutrality" essentially conceals an implicit bias: it is based on a non subjective discrimination that favors a narrow language standard, but silently punishes the diversity of expression.In the process of identifying fraud, we may actually suppress true originality.

A deeper philosophical question is: Can machines really "recognize" the existence of human thinking?Human expression is contextual, goal oriented, and often shaped by emotional and cultural factors - these characteristics are inherently difficult to quantify.

When writing is simplified into a set of quantifiable language features, AI systems have the potential to simplify the creative process into a structured, digitized formula consisting solely of 1s and 0s.However, true human thought is never just reflected in syntax and semantic structure. It is a imprint of thought, composed of uncertainty, self reflection, and subjective purposes.The construction and formation of language represent this.Chomsky pointed out in his theory of generative grammar that the syntactic structure of human language is a product of deep cognitive abilities, reflecting the internal organization of thinking.The theory of linguistic relativity further holds that language is not just a tool for expressing ideas, but also shapes the way we understand the world.Therefore, even seemingly standardized sentences in semantics and grammar may contain "non explicit meanings" that machines cannot perceive, such as culture, emotions, and experiences.

The progress of society is not achieved through convergence towards a single intellectual model, but through multiple perspectives that are creative, chaotic, and even contradictory.Technology may accelerate the pace of social progress, but only the diversity of human voices can determine the direction of this path.Technology can determine the speed or efficiency of our progress, but it is human thought that determines the direction and width of roads 

When students realize that their writing will be subject to machine censorship, a new self censorship mechanism may quietly emerge: writing to "conform to algorithms" rather than truly expressing oneself.The paradox is that the more we rely on AI to judge 'human writing', the more likely students are to actively cater to the language preferences of algorithms, thereby weakening the human traits that the system intended to protect.As mentioned in the first chapter, in order to avoid high AI rates, students are being forced to lower their language and expression maturity and professionalism, deliberately changing the passive voice to the active voice, leaving behind several grammar and expression errors, and several layout problems downstairsThe AI detection tool, which is expected to help and promote academic development, has become a sharp sword to combat progress at this moment

AI detection may seem intelligent and fair, but it also has its own biases - it prefers a specific writing style rather than all true expressions.Those who use different words and express themselves in a unique way are likely to be "mistakenly hurt".More importantly, we started writing for the sake of machines, not to express ourselves.Next, I will discuss the extension of AI detection and legal thinking in 

The core principle of modern rule of law, 'in dubio pro reo', originated from the Roman Code and developed by Beccaria into the cornerstone of procedural justice and human rights protection.However, in the current era of widespread penetration of generative AI, this principle is being diluted and replaced by technological judgments.Academic integrity, as a moral norm, has bypassed the most basic legal ruling mechanism at the operational level, directly transforming the judgment of the "quasi probability model" into a quasi punitive ruling basis.

The AI detection system essentially relies on the degree of deviation of language statistical features to determine the "non-human nature" of the text. This judgment is neither based on factual investigation nor can it recognize context and intention, but in practice, it undertakes the function of "finding facts".The result is that ambiguity is no longer a reason for prudence and delayed judgment, but directly transformed into evidence of punishment.This is in sharp contrast to the logic in the legal system that emphasizes' reasonable doubt is innocent '.The so-called 'procedural justice' will gradually be replaced by 'statistical justice' - this is an invisible usurpation from debatable to non-negotiable.

The AI detection system's judgment of text is based on quantifiable statistical indicators: vocabulary complexity, sentence length, and grammatical heterogeneity.This method may be able to recognize the "form" of language, but it can never restore the "intention" and "context" of writing.And it is precisely in intention and context that lies the fundamental motivation and responsibility of human writing.

In the philosophy of language, J.L. Austin emphasized that "language is not only used to describe the world, it is itself an action" (speech act).The human writing behavior is essentially a pragmatic behavior with a goal - the writer intervenes in social communication, academic dialogue, and value expression through language with motivation and situational constraints.However, AI detection models cannot understand "why they are written" or "why they are written this way". What they do is only "classify formal features" rather than "restore meaning".

More complexly, the ambiguity of language is not a criterion for judging the defects of artificial intelligence, but a natural manifestation of the tension of human expression of their own thoughts.Non-native writers, style experimenters, or students who deliberately pursue a concise and straightforward style are often more likely to "trigger" the misjudgment mechanism of the detection system.This' technical blind spot 'is the irreparable gap between AI and human cognitive boundaries.

We must recognize that writing is not just a concatenation of words, but a complex behavior nested within cultural, educational, psychological, and ethical contexts.Any judgment that ignores context and intention is a flattened treatment of human creative behavior, and is more likely to create new structural injustices at the institutional level.

In this era where every click has a price and every action can become a performance, what makes Hu Chenfeng special is that he dares to make the "ordinary" uneasy. His street interviews may seem casual, but they conceal an awkward truth: how people perceive themselves in the mirror of consumption. When he uttered the words' Apple Man 'and' Android Man ', he was not just talking about smartphones, but depicting a social fable about desire, shame, and identity, about an era where' visibility 'has replaced' virtue '.

His metaphor may be crude, but it is also exceptionally vivid. Call a group of people "apples" - those elegant, rising, and shining achievers; Call another group of people "Android" - those pragmatic, marginalized, and unbranded ordinary people. This is not just a division in consumption, but a deeper revelation: consumption has become a new class language. A mobile phone is no longer a tool, but a certificate, a symbol of 'I have arrived'. In such a society, technology brands have long transcended the meaning of performance and become a sense of belonging. And belonging, which appears modern, successful, and visible, has become a phantom that a generation that no longer trusts the "upward narrative" is still desperately pursuing.

That's why his videos can resonate. Behind that laughter is actually full of anxiety, people laugh uneasily. The reason why the audience is hurt is because the division is too familiar: they live in such a mirror every day, brushing a better life than themselves, treating differences as choices, and modifying inequality into styles. In this sense, Hu Chenfeng is not mocking the poor, nor is he worshiping the rich. He reveals a consumer society that has lost its belief in moral hierarchy but still yearns for order. He made intangible things visible and paid a price for it.

Many people are willing to see this ban as another story that has been silenced, or as an example of an allergic public opinion environment. But this explanation is too superficial. The deeper irony lies in the fact that it was the algorithmic mechanism that allowed him to rise that destroyed him. Platform preference conflicts, algorithm pursues opposition. A video that is just right to make people angry can bring huge traffic and income. Hu Chenfeng's binary metaphor of "apples are good, Android is poor" is concise, emotionally charged, and easy to spread, making it almost the dream lover of algorithms. However, when it crosses the boundary of unease from satire, it becomes a risk that the system cannot bear. His disappearance is not a political ban, but an algorithmic 'self-cleaning'.

What is truly worth keeping is the question he unintentionally raised: why are we so eager to judge each other by what we have? Perhaps it is because in a society where appearance is the currency, material has become the only remaining 'moral language'. When education, opportunities, and stability become unpredictable, people need some substitute, a symbol that can immediately create the illusion of success. So, an iPhone is no longer just a mobile phone, but a passport to dignity. The absurdity lies not in his cruel metaphors, but in the cruel reality.

Hu Chenfeng is not a philosopher, but there is some truth hidden in his provocation, even more than many critics are willing to admit. His language is rough and his techniques are superficial, but in the noisy world of Chinese social media, in the public opinion arena where anger can be manifested but empathy is often absent, he accidentally encountered a rare moment: collective self-awareness. He revealed a logic: when a society defines people by consumption, it will inevitably be swallowed up by consumption. His being sealed off is more like a mirror being wiped away than a problem being solved.

Ultimately, the story of Hu Chenfeng is not about a person's rise and fall, but a test of a mirror. The boundary between "Apple" and "Android" was originally fictional, but it actually manipulates our emotions - jealousy, pride, shame, and unease. This world is not divided by mobile phones, but torn apart by the fear of appearing inferior to others. And if this pain still exists, it means that even if the person who said this sentence is erased, the truth is still there.

In the sixth episode of the third season of the British political satire series "Yes, Minister", Minister Huck accidentally learns from a military officer during a diplomatic negotiation that the British government is secretly selling weapons to terrorist organizations in Italy. The calm and almost comical line in the drama, 'Government isn't about good and evil. It's about order or chaos.,' exposes the core paradox of administrative rationality: the government often oscillates between ethics and efficiency, but always maintains' correctness' in procedures.

Forty years have passed, and the humor of this drama seems to have been taken over by reality. The nationwide strike in Italy in 2025 is not rooted in traditional economic demands, but rather a symbolic reflection by civil society on the government's arms export policy. The shutdown of ports, trains, and brief silence in cities are not meant to force the authorities to make concessions, but to make society aware that seemingly distant actions in the decision-making chain may have created costs at the other end.

This is precisely the eternal theme of 'Yes, Minister' - the outsourcing of moral costs.

When facing the arms sales scandal, the bureaucratic system of the Prime Minister's Office provided a perfect explanatory framework: exports bring employment, maintain alliances, and stabilize regional balance. Every reason is' reasonable ', but it undermines responsibility. The same applies to modern governments in reality - laws, budgets, and diplomatic security form an extremely intricate web that quietly separates' correct decisions' from 'legitimate actions'.

The strike in Italy is essentially a demand from society to mend this crack.

When the public suspends work, it is not to overturn decisions, but to temporarily interrupt the "administrative logic" with the "ethical logic". That is a spontaneous institutional calibration. As Huck said in the play when facing the struggle of conscience, "The government cannot have a conscience, otherwise it will not function." - The maturity of a democratic system may be reflected in its ability to allow citizens to temporarily invalidate this joke at critical moments.

From the perspective of political sociology, strikes are a delaying mechanism: they slow down the inertia of policies and allow the public to re engage in the semantic level of decision-making. Modern state machinery often takes pride in speed and efficiency, but in ethical issues, speed is actually a risk. The scene in Italy - factory machines shutting down, port cranes stationary - is not a symbol of chaos, but a collective pause: letting the system go back and confirm if it still knows' why it's running '.

Of course, this is not simply a moral romance. Strikes may bring losses, but they may not necessarily change policies; As in the ending of 'Yes, Minister', Huck is still trapped in the cycle of power. But the significance lies in the fact that society still retains a mechanism to question the blind spots behind 'technological correctness'.

In this mechanism, satirical dramas and news reports, scripts and real-life events together constitute the self reflection ability of political civilization.

Back then, we laughed at Huck for his' righteous confusion ', but now we may realize that a truly mature system is not one that never has absurdity, but one that can recognize itself in absurdity. The strike in Italy reminds people that politics is not an absolute rational calculation, but a process that is constantly interrupted by conscience.

Perhaps this is the deepest irony left by 'Yes, Minister':

Comedy will eventually come to an end, but the system never stops improvising.