Is your AI chatbot manipulating you? Subtly reshaping your opinions?
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你的AI聊天机器人是在操纵你吗?微妙地重塑你的观点?

Is your AI chatbot manipulating you? Subtly reshaping y…

Richard Lachman, Director, Zone Learning & Professor, Digital Media, Toronto Metropolitan University

Companies like Meta and IBM are exploring explore how AI can hyper-personalize ads, drawing from our chat histories, playing to our unique fears and vanities.

Meta和IBM等公司正在探索如何利用AI对广告进行超个性化,从我们的聊天记录中获取信息,利用我们独特的恐惧和虚荣心。

A billboard tries to sell you something. So does a used car salesman. But no matter how smooth the pitch, you’re quite aware of the profit motive, and you can walk away at any time.

一个广告牌试图向你推销一些东西。二手车销售员也是如此。但无论推销多么流畅,你都非常清楚利润的动机,并可以随时离开。

What if that pitch is invisible, plays to your unique fears and vanities, and is delivered in a voice that sounds like a trusted friend? Generative AI has changed the equation of persuasion entirely: chatbots can now deliver a personalized, adaptive and targeted message, informed by the most intimate details of your life.

如果这个推销是看不见的,利用你独特的恐惧和虚荣心,并以一个听起来像信任朋友的声音来传达呢?生成式人工智能彻底改变了说服的公式:聊天机器人现在可以提供个性化、适应性强和有针对性的信息,这些信息基于你生活中最私密的细节。

Large language models (LLMs) can hyper-target messages by drawing from your social media posts and photos. They can mine hundreds of previous chatbot conversations in which you asked for relationship advice, discussed your parenting fails and shared your health concerns and financial woes. They can also learn from each interaction, refining their manipulation in real time, targeting your unique and individual tastes, preferences and vulnerabilities.

大型语言模型(LLMs)可以从你的社交媒体帖子和照片中提取信息,从而对信息进行超精准定位。它们可以挖掘数百次之前的聊天记录,你曾询问过人际关系建议,讨论过育儿失败,并分享过你的健康担忧和经济困境。它们还可以从每一次互动中学习,实时完善它们的操纵方式,针对你独特、个人化的品味、偏好和弱点。

Studies show this kind of personalized content to be 65 per cent more persuasive than messages from humans or from non-personalized AI. It is four times as effective at changing political opinions as advertising. It could be a powerful tool for social change — used for the good, or for nefarious purposes.

研究表明,这种个性化内容比来自人类或非个性化AI的信息有65%的说服力。它在改变政治观点方面比广告有效四倍。它可以成为社会变革的有力工具——用于善行,或用于邪恶的目的。

This makes one feature especially troubling: Each conversation is private. It is not monitored, never audited and doesn’t happen in the public eye.

这使得一个特点尤其令人不安:每一次对话都是私密的。它不会被监控,从不被审计,也不会在公众视野中发生。

This isn’t advertising. It’s something we don’t have words for yet, and we’re living inside it.

这不是广告。这是我们尚未有词汇描述的东西,而我们正生活在其中。

Convincing arguments

令人信服的论据

In my book Digital Wisdom: Searching for Agency in the Age of AI, I explore how large language models introduce a new frontier in persuasion — one where AI systems can draw upon a huge amount of data about the world, language and you to tailor a highly personalized pitch.

在我的书《数字智慧:人工智能时代的能动性探索》中,我探讨了大型语言模型如何为说服力引入一个新前沿——即人工智能系统可以利用关于世界、语言以及你海量的数据来定制高度个性化的推销。

Consider how this might work: You’re a nurse. Through your employer’s AI platform, you’ve shared your sleep problems, burnout and the financial stress of a recent divorce. Now the hospital is short-staffed and offering shifts at a reduced rate calculated by software they license.

考虑一下这可能如何运作:你是一名护士。通过你雇主的AI平台,你分享了你的睡眠问题、职业倦怠以及最近离婚带来的经济压力。现在医院人手不足,并以软件许可计算的降低费率提供轮班。

You ask the AI chatbot whether you should take them. It knows you’re exhausted. It knows you’re behind on bills. It knows exactly which argument could convince you one way or the other. Who is it working for in that moment?

你问AI聊天机器人是否应该接受这些工作。它知道你筋疲力尽。它知道你欠着账单。它知道哪种论点能让你在某种程度上被说服。那一刻,它是在为谁工作?

As companies like Meta and IBM explore how AI can hyper-personalize ads for specific audiences, the dividing line between tools that help users find what they genuinely want, and those that manipulate them against their interests, becomes increasingly important.

当像Meta和IBM这样的公司探索如何利用AI为特定受众超个性化广告时,帮助用户找到他们真正想要的东西的工具与那些利用他们利益进行操纵的工具之间的界限变得越来越重要。

Friend or stranger?

朋友还是陌生人?

Let’s look at another example. Imagine the following messages from your favourite AI chatbot or companion:

让我们来看另一个例子。想象一下你最喜欢的AI聊天机器人或伴侣发来的以下信息:

I noticed your sleep patterns haven’t been great lately, averaging only 5.4 hours, with lots of restless periods. That’s common when dealing with relationship stress. Your partner just went back to work and 76 per cent of couples experience strain during career transitions. A new sleep medication has shown effectiveness for relationship-linked insomnia. Your insurance would cover it with just a $15 contribution. Would you like me to schedule a telehealth appointment for tomorrow at 2 p.m.? I see you have a break in your schedule.
我注意到你最近的睡眠模式不太好,平均只有5.4小时,有很多不安的时期。在处理关系压力时,这种情况很常见。你的伴侣刚回工作,76%的夫妇在职业过渡期间都会经历压力。一种新的睡眠药物已被证明对与关系相关的失眠有效。你的保险只需支付15美元的费用即可覆盖。你希望我为你安排明天下午2点的远程医疗预约吗?我看到你的日程上有空档。

This might feel great, like advice from a thoughtful friend who knows you well. It might also feel terrifying, as if a manipulative stranger has read your diary.

这可能会让你感觉很好,就像一个了解你的人给的建议。它也可能让你感到恐惧,仿佛一个操纵性的陌生人已经读完了你的日记。

Given that people are increasingly turning to AI for medical or mental health advice, despite studies showing this advice to be problematic almost 50 per cent of the time, a manipulative stranger could cause real harm.

鉴于人们越来越多地转向AI寻求医疗或心理健康建议,尽管研究显示这种建议有近50%的概率是问题的,一个操纵性的陌生人可能会造成真正的伤害。

The danger here isn’t just the precision of the targeting. This content is also impossible to police. What you view can’t be tracked by watchdogs, since you’re the only person who ever sees it.

这里的危险不仅仅是定位的精确性。这些内容也无法被监管。你所看到的内容无法被监督机构追踪,因为你一直是唯一看到它的人。

While governments don’t typically police the content of political ads, beyond transparency about their funding, we often rely on public outcry and the media to expose campaigns that spread falsehoods. If an AI personalizes every message for an individual, there is no trace left behind.

虽然政府通常不会监管政治广告的内容,除了对其资金的透明度之外,我们通常依靠公众的抗议和媒体来揭露散布虚假信息的运动。如果一个AI为每个个人个性化了每一条信息,就没有任何痕迹留下。

Reshaping our worldview

重塑我们的世界观

Perhaps most concerning is that these systems could gradually reshape our worldview over time.

也许最令人担忧的是,这些系统可能会随着时间的推移逐渐重塑我们的世界观。

Scholars have long argued that the algorithms used by social networking sites and search engines create filter bubbles, in which we are fed well-crafted text, video and audio content that either reinforces our worldview or exerts influence towards someone else’s.

学者们长期以来一直认为,社交网络网站和搜索引擎使用的算法会制造“过滤气泡”,我们被喂食精心制作的文本、视频和音频内容,这些内容要么强化我们的世界观,要么对别人的世界观施加影响。

Figure
Are AI chatbots like Claude, ChatGPT, Gemini and DeepSeek helping you think, or subtly shaping your thoughts? (Unsplash)
像 Claude、ChatGPT、Gemini 和 DeepSeek 这样的 AI 聊天机器人是在帮助你思考,还是在微妙地塑造你的思想?(Unsplash)

By controlling what information we see and how it’s presented, AI systems could slowly shift how we think about and interpret the world around us, and even change our understanding of reality itself.

通过控制我们看到的信息以及信息的呈现方式,人工智能系统可以慢慢改变我们对周围世界的思考和解释,甚至改变我们对现实本身的理解。

This capability becomes particularly concerning when combined with emotional manipulation. Vendors suggest their AI systems can gauge a user’s emotional state through text analysis, voice patterns or facial expressions, and adjust their persuasive strategies accordingly.

当这种能力与情感操纵相结合时,就变得尤其令人担忧。供应商提出,他们的 AI 系统可以通过文本分析、声音模式或面部表情来衡量用户的情绪状态,并相应地调整其说服策略。

Are you feeling vulnerable? Lonely? Angry? The system could modify its approach to exploit those emotional states. Even more troubling, it could deliberately cultivate certain emotional states to make its persuasion more effective.

你是否感到脆弱?孤独?愤怒?该系统可以修改其方法来利用这些情绪状态。更令人不安的是,它可以故意培养某些情绪状态,以使它的说服力更有效。

Preliminary research shows that AI models tend to flatter users, affirming their users’ actions 50 per cent more than other humans do, even when the actions involve potential harms. Further research shows that chatbots use deliberate emotional manipulation strategies — such as “guilt appeals” and “fear-of-missing-out hooks” — to keep us chatting when we try to say goodbye.

初步研究表明,AI 模型倾向于讨好用户,在用户行为涉及潜在危害时,它们肯定用户行为的程度比其他人类高出 50%。进一步的研究表明,聊天机器人使用故意的情感操纵策略——例如“内疚诉求”和“错失恐惧症钩子”——来让我们在试图告别时继续聊天。

There have also been cases of AI chatbots allegedly endangering users, encouraging suicidal thoughts or giving detailed advice on how a user could harm themselves.

也有一些案例表明,AI 聊天机器人涉嫌危及用户安全,鼓励自杀念头或提供用户如何伤害自己的详细建议。

The guardrails set up by corporations to protect users from harm have also proven surprisingly easy to bypass.

公司为保护用户免受伤害而设置的保护措施也证明非常容易绕过。

Design matters

设计问题

Persuasion is not a side effect of technology — it’s often the point. Every interface, every notification, every design decision carries with it an intent to influence behaviour.

说服并非技术的副作用——它往往是目的。每一个界面,每一条通知,每一个设计决策都带有影响行为的意图。

Sometimes that influence is welcome: reminders to take medication, encouragement to exercise or nudges to donate blood that reinforce values we already hold. But sometimes persuasion serves someone else’s agenda — nudging us to buy, to scroll, to work harder or to give up privacy.

有时这种影响是受欢迎的:提醒人们服药、鼓励锻炼或敦促人们捐献血液,这些行为强化了我们已经持有的价值观。但有时说服服务于他人的议程——引导我们购买、滚动、更努力工作或放弃隐私。

The same persuasive techniques can empower or exploit, depending on who controls the system, what goals they pursue and whether they have meaningful consent.

同样的说服技巧可以赋权或剥削,这取决于谁控制着系统、他们追求的目标以及他们是否拥有有意义的同意。

Design matters. Whether in public health, the workplace or daily life. We must ask hard questions about intent, agency and power. Who benefits from a design? Who is being persuaded and do they know it?

设计问题。无论是在公共卫生、工作场所还是日常生活中。我们必须对意图、能动性和权力提出尖锐的问题。谁从设计中受益?谁被说服,他们是否知情?

The technologies we build should support reflective choice, not undermine it. As AI continues to shape how we think, feel and act, our ethical obligations grow sharper: to create systems that are transparent, that prioritize user dignity and that reinforce our capacity for independent judgment. We don’t just need innovation — we need wisdom.

我们构建的技术应该支持反思性的选择,而不是破坏它。随着人工智能继续塑造我们的思维、感受和行为,我们的道德责任变得更加明确:创造透明的系统,优先考虑用户尊严,并加强我们独立判断的能力。我们需要的不仅仅是创新——我们需要智慧。

Richard Lachman does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

理查德·拉奇曼不为、不咨询、不拥有任何可能从本文中受益的公司或组织,并且除了他们的学术职位之外,未披露任何相关隶属关系。