当AI成为社会的眼睛:技术赋权下的监督幻觉与权力博弈
从Karpathy对AI提升政府透明度的乐观展望,到BBC调查所揭示的精英内幕交易,再到阴谋论社区对深层权力的恐惧,我们正处在一个技术承诺与社会现实激烈碰撞的十字路口。本文试图论证,技术工具本身无法解决政治问责的根本困境,真正的透明度革命需要的不是更强大的算法,而是对权力运行逻辑的彻底重构。
核心观点:AI技术看似为公民监督政府提供了前所未有的工具,但这种技术赋权本质上是一场幻觉,因为它忽略了权力结构对信息流动的根本性控制,以及技术本身可能被反向收编为更精密的控制工具。
当AI领域的顶尖思考者Andrej Karpathy在社交媒体上畅想一个由AI赋能的、公民能够以前所未有的清晰度‘阅读’和监督政府的未来时,他描绘的图景无疑是诱人的。在他的设想中,那些堆积如山的立法文件、错综复杂的预算案、晦涩难懂的监管条文,都将被AI转化为普通人可理解、可追踪、可分析的透明信息流。政府行为的‘可见性’和‘可读性’将不再是少数专业记者或研究人员的特权,而是每个公民触手可及的能力。这种技术乐观主义的核心在于一个假设:政府问责的主要障碍是信息的‘处理能力’而非‘获取权限’。然而,当我们把目光投向现实世界——比如BBC调查所暗示的白宫与金融市场之间那‘统计上不可能’的同步性,或是Reddit阴谋论板块中流传的关于肯尼迪家族因‘难以控制’而遭惩罚的叙事——就会发现,Karpathy的乐观模型可能建立在过于天真的前提之上。
问题的核心不在于技术能否解析4000页的综合法案,而在于权力是否愿意让这4000页以真实、完整、未经修饰的形式存在,并接受算法的审视。现代政府的‘透明度’往往是一种精心设计的表演。数据被大量公布,但其格式混乱、标准不一、关键部分可能被归类为‘国家安全’或‘行政特权’而屏蔽。AI可以处理海量数据,但它无法创造数据。当权力的核心运作——例如高级官员与金融寡头在非正式场合的密谈、情报机构的秘密行动、监管机构与行业巨头之间的旋转门交易——始终处于阴影之中时,再强大的自然语言处理模型也只能在官方叙事的表面打转。BBC的调查之所以引发震动,正是因为它试图刺破这层表面,通过交易数据的异常模式来间接推断不可见的权力勾连。然而,这种调查本身也面临着巨大的阻力:信息来源的匿名性、数据获取的法律灰色地带、以及来自既得利益集团的反扑。AI在此类调查中或许能充当放大镜,但它无法替代调查记者所需的勇气、人脉以及对权力运作的直觉性理解。
更进一步说,技术赋权的乐观叙事常常低估了权力结构对技术本身的收编和反向利用能力。历史反复证明,任何能够增强社会‘可见性’的工具,几乎总是率先被国家机器用来增强其对社会的‘可见性’与控制力。从户籍制度到摄像头网络,从大数据征信到社会信用体系,技术的矛头往往最终指向公民,而非权力本身。AI驱动的社会监督工具,完全可能被改造为更高效的舆情监控系统、更精准的政治异见者识别模型,或是为选择性执法提供‘数据支撑’的算法黑箱。当Karpathy提到‘同样的工具很容易走向反面’时,他触及了问题的边缘,但未深入其骨髓:在一个权力高度不对称的体系中,技术工具从来不是中立的。掌握更多资源、数据和强制力的政府与资本联盟,在开发和部署监督性AI方面,注定比分散的公民社会拥有压倒性优势。公民用AI分析预算案的同时,权力可能正在用更先进的AI预测和化解可能由此引发的舆论风险。
这种博弈在信息生态的层面表现得尤为明显。我们当前所处的,是一个‘真相’与‘叙事’激烈争夺注意力的战场。一方面,有BBC式的传统调查新闻试图基于事实和数据建立问责;另一方面,Reddit的阴谋论板块则充斥着关于光明会、深层政府、肯尼迪遇刺背后秘密权力的宏大叙事。后者虽然缺乏实证支撑,但其情感号召力和对世界提供‘简单’解释的能力,使其在特定群体中产生了巨大的影响力。AI技术在这个战场上扮演着矛盾的角色:它既可以用来进行事实核查、追踪虚假信息网络,也可能被用来生成更具迷惑性的深度伪造内容、自动化生产迎合特定偏见的宣传材料,或是通过算法推荐将用户困在信息茧房中。当Karpathy期待AI让政府更‘可读’时,他或许没有充分考虑到,政府及其对手方同样可以利用AI让自身变得更‘不可读’,或至少是塑造一种对其有利的‘可读性’。
因此,对AI赋能社会监督的讨论,必须跳出纯粹的技术功能主义视角,进入政治哲学和权力社会学的范畴。透明度和问责制不是一个可以靠提升‘信息处理带宽’就能解决的工程学问题。它是一个关于权力分配、制衡机制、公民权利以及政治文化的问题。技术可以改变博弈的成本和效率,但无法改变博弈的根本规则。一个健康的民主社会,需要的不仅仅是公民拥有分析政府数据的技术能力,更需要保障言论自由、新闻独立、司法公正的制度环境,以及一个积极参与、具有批判性思维的公民群体。没有这些制度和文化基础,AI监督工具要么沦为摆设,要么成为权力博弈中又一枚被扭曲的棋子。
这并不是要全盘否定技术可能带来的积极变化。AI确实有可能降低专业监督的门槛,让更多公民团体、小型媒体和非营利组织能够开展曾经只有大型机构才能负担得起的调查。它也可能通过模式识别,发现人力难以察觉的系统性腐败或违规迹象。但这些积极潜能的实现,有赖于一个前提:技术发展的方向必须由追求公益的社会力量来引导和塑造,而不是完全由商业利益或政府管控的需求所主导。这要求技术社区本身具备更强的社会责任感,在开发工具时深入思考其政治和社会影响,并积极与公民社会组织合作。同时,法律和政策必须跟上,确保关键公共数据的真正开放(而不仅仅是发布),保护基于数据的调查性新闻和公民监督行为免受不当的法律打压。
最终,我们面临的挑战是双重的:一方面,要利用技术工具去刺探权力的黑箱,哪怕只能撬开一丝缝隙;另一方面,又要警惕技术本身被权力收编,成为加固黑箱的新工具。这是一场永无止境的猫鼠游戏。Karpathy的乐观提醒我们技术蕴含的解放潜力,而BBC的调查和阴谋论的盛行则警示我们权力根深蒂固的隐匿本能。或许,真正的进步不在于幻想技术能一劳永逸地解决透明度问题,而在于承认这场博弈的永恒性,并不断锻造和更新我们参与这场博弈所需的工具、制度与勇气。在这个意义上,AI只是漫长斗争中的一个新变量,它既不是救世主,也不是终结者,它只是放大了人类社会中那个古老问题的紧迫性:谁有权看见?谁必须被看见?
参考来源
- "The Kennedy family were punished because they tried to disobey them. They were free thinkers, and too hard to control." - https://www.reddit.com/r/conspiracy/comments/1sraxhp/the_kennedy_family_were_punished_because_they/
- Something I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.
- Historically, it is the governments that act to make society legible (e.g. "Seeing like a state" is the common reference), but with AI, society can dramatically improve its ability to do this in reverse. Government accountability has not been constrained by access (the various branches of government publish an enormous amount of data), it has been constrained by intelligence - the ability to process a lot of raw data, combine it with domain expertise and derive insights. As an example, the 4000-page omnibus bill is "transparent" in principle and in a legal sense, but certainly not in a practical sense for most people. There's a lot more like it: laws, spending bills, federal budgets, freedom of information act responses, lobbying disclosures... Only a few highly trained professionals (investigative journalists) could historically process this information. This bottleneck might dissolve - not only are the professionals further empowered, but a lot more people can participate.
- Some examples to be precise: Detailed accounting of spending and budgets, diff tracking of legislation, individual voting trends w.r.t. stated positions or speeches, lobbying and influence (e.g. graph of lobbyist -> firm -> client -> legislator -> committee -> vote -> regulation), procurement and contracting, regulatory capture warning lights, judicial and legal patterns, campaign finance... Local governments might be even more interesting because the governed population is smaller so there is less national coverage: city council meetings, decisions around zoning, policing, schools, utilities...
- Certainly, the same tools can easily cut the other way and it's worth being very mindful of that, but I lean optimistic overall that added participation, transparency and accountability will improve democratic, free societies.
- (the quoted tweet is half-ish related, but inspired me to post some recent thoughts) - https://nitter.net/karpathy/status/2040549459193704852#m
- RT by @paulg: A new paper in @Nature from David Reich, @aliakbari23 and colleagues breaks the conventional understanding of recent human evolution. The field believed that strong selection in the recent past (~10,000 years) was rare, with few exceptions like the lactase persistence locus. In this paper, the authors challenge that belief, showing that we weren't looking at the problem right.
- Previous studies that looked for evidence of selection using ancient DNA addressed the problem cross-sectionally, asking if allele frequencies differed across populations more than what one would expect based on genetic drift and migration. Most arrived at the conclusion that population structure primarily explained the observed differences. Here, the authors addressed the problem longitudinally, accounting for when ancient individuals lived by explicitly modeling time as a variable in the analysis. It turns out doing it this way dramatically increases power, increasing the number of genome-wide significant selection signals by 20-fold!
- Looking at why accounting for the time variable led to such dramatic changes in results, the authors find that previous studies missed so much because selection often happened not on new variants leading to dramatic sweeps (the conventional model: new variant -> selection -> increase in frequency) but on already existing variants driven by transient environmental pressures. Many of these variants underwent reversals, selected up when a pressure existed, then purged when it disappeared or the trade-off cost became dominant. A great example is the TYK2 variant, where an allele boosting immunity was selected for thousands of years because it protected against TB, then got purged as TB endemicity declined and the autoimmune cost took over.
- The scale of what they found is striking: hundreds of loci showing strong selection in the past 10,000 years with a median selection coefficient of ~0.86%. This number is pretty big in evolutionary terms, meaning allele frequencies have been shifting by ~1% per generation in a consistent direction. Previous selection scans found a maximum of 20 loci, and this one finds hundreds. That isn't an incremental change. It fundamentally reframes our understanding of how common strong selection has been in recent human history.
- Some of the most striking findings come from polygenic selection, where hundreds of small-effect alleles were pushed in the same direction simultaneously. Polygenic scores based on large-scale GWAS of today predict recent negative selection for traits like body fat, waist circumference and schizophrenia, and positive selection for others like cognitive traits. One important caveat is that GWAS phenotypes are measured in industrialized societies today, and how well they capture what was actually being selected in ancient environments is debatable.
- For me personally, these findings have direct implications for drug discovery. When using human genetics to find drug targets, we often fixate on the benefit and risk profiles of variants visible today. But we need to be aware that a variant's benefit:harm ratio might be environmentally contingent, and could reverse when the wrong environment manifests. An evolutionary understanding of a variant's association with traits is therefore essential.
- The same logic applies, perhaps even more urgently, to embryo selection. Selecting embryos based on polygenic traits is humans making permanent, heritable decisions for their offspring with a narrow view of today's environment. The ancient DNA record now shows that cost-benefit landscapes flip over time. So, an embryo carrying man-made selections is carrying those changes into an unpredictable future environment.
- The broader takeaway is that human evolution didn't freeze in the last 10,000 years. We just lacked the tools and datasets to see its movement. The current findings are based on European populations. I am curious to see these analyses extended to other populations too, like South Asian, East Asian and African populations, which might be holding more surprises to blow our minds.
- Akbari et al. Nature 2026
- https://www.nature.com/articles/s41586-026-10358-1 - https://nitter.net/doctorveera/status/2044679999450664967#m