AI之眼凝视国家:当技术赋权试图倒转“利维坦”的望远镜
从冗长的法案到复杂的财政数据,政府运作的“黑箱”向来只对少数专业人士敞开。AI似乎带来了曙光:它能解析海量信息,让每个公民都成为监督者。但这真的是一场权力的民主化革命吗?还是说,我们只是用更精密的技术工具,重复着古老的监督困境?当AI之眼试图凝视国家机器,它看到的可能不仅是透明,更是权力博弈深水区中那些难以被算法量化的暗流。
核心观点:AI技术赋予社会个体前所未有的信息处理能力,理论上能倒转传统“国家审视社会”的权力视角,实现社会对国家的深度监督;然而,这一技术乐观主义愿景忽视了监督本身的政治性、数据的结构性壁垒以及技术可能被权力反向收编的复杂现实,最终可能强化而非消解现有的不对称权力结构。
想象一下,一位普通公民,利用AI工具,轻松解析了一份长达四千页的政府综合拨款法案。工具不仅概括了要点,还高亮显示了新增的专项拨款,追溯了提出相关条款议员的投票记录,并关联了这些议员背后主要竞选捐款人的行业利益。几分钟内,一份原本需要专业记者团队数周分析的报告,清晰呈现。这是AI技术乐观主义者所描绘的美好图景:技术赋权于民,倒转詹姆斯·C·斯科特笔下“国家试图使社会清晰化”的经典过程,让社会能够以前所未有的清晰度来审视国家。监督的瓶颈从“信息获取”转向“信息处理”,而AI正是打破这一瓶颈的终极利器。材料中,知名AI学者也表达了对此的乐观预期,认为这将增加参与、透明度和问责,从而改善自由民主社会。
这一愿景在技术上无疑是激动人心的。AI,特别是大型语言模型,在信息提取、关联分析、模式识别和自然语言总结方面的能力,确实能将大量非结构化的政府公开数据(法律文本、预算文件、议会记录、游说披露、采购合同、司法文书)转化为普通人可理解的洞察。监督的范围可以极大扩展,从国家层面的立法博弈,到地方市议会的分区决议、警务政策、学校经费等细微之处。理论上,这能形成一种分布式、实时化的社会监督网络,提高权力滥用的风险,促使公职人员更加负责。
然而,当我们从技术可行性的兴奋中冷静下来,将目光投向政治社会学和权力运作的现实层面时,这幅图景便开始出现裂痕。第一个悖论在于:监督行为本身从来不是价值中立的,它自始至终都是政治性的。AI工具并不会自动告诉用户“应该监督什么”以及“如何解释监督结果”。监督议程的设置——是关注国防开支的浪费,还是社会福利项目的效率?是追踪环境监管的松懈,还是审查文化政策的倾向?——本身就反映了特定的意识形态、利益关切和权力视角。开发这些AI工具的个人或组织,其自身的价值观和认知框架,会通过训练数据的选择、提示词的设计、分析维度的优先级,深深地嵌入到工具之中。一个由自由市场主义者构建的预算分析工具,与一个由社会公平倡导者构建的工具,对同一份财政案可能会给出截然不同的“关键发现”。因此,AI赋权下的社会监督,可能不会导向一个更统一的、基于事实的公共领域,反而可能加剧基于不同价值观和世界观的“监督泡沫”,让不同群体用各自强大的AI武器,为自己预先持有的立场挖掘“证据”,进一步极化公共辩论。
第二个更为根本的挑战,是数据本身的结构性壁垒。技术乐观主义的假设是,监督所需的数据都是“可得的”、“公开的”。但现实是,政府信息的公开程度、颗粒度、机器可读性以及关键元数据的完整性,本身就是权力博弈的结果。真正敏感的权力运作,往往不会留下易于追踪的电子痕迹,或者以高度概括、模糊的语言进行编码。游说活动最有效的部分发生在非正式的晚宴和电话中;政策制定的关键妥协可能发生在会议室关闭的大门后;行政决策的裁量空间可以巧妙地隐藏在复杂的法规条文里。AI可以处理已公开的文本,但它无法创造不存在的数据。如果权力有意将自身活动置于“可监督的黑暗”中,那么再强大的AI工具也只能在数据的光亮区域内打转,甚至可能因其分析能力而产生一种“我们已经洞悉一切”的错觉,反而掩盖了真正的盲区。
最值得警惕的,是技术被权力反向收编和利用的可能性。历史反复证明,任何能够增强社会能力的工具,几乎都会被权力体系尝试吸纳、改造,用于巩固自身。AI驱动的社会监督工具,完全可以被权力反过来用于监控社会。政府可以建立更强大的AI系统,实时分析公民的监督行为模式、关注焦点和舆论倾向,从而更精准地进行宣传引导、议程设置甚至压制。这并非危言耸听,它正是“棱镜”计划在大数据时代的AI升级版。此外,权力机构也可以利用AI技术,制造更复杂、更难以被普通AI工具穿透的信息迷雾(如生成海量的、看似合规但无实质内容的文件),或者发起针对性的信息战,用AI生成误导性的“证据”来混淆视听、诋毁真正的监督者。在这场AI赋权的军备竞赛中,拥有庞大资源、数据和控制力的国家机器,很可能最终占据不对称的优势。
因此,AI赋权社会监督的叙事,可能过于天真地假设了技术力量能够直接转化为政治权力结构的改变。它忽略了监督的本质是一场持续的权力斗争,而技术只是这场斗争中的一件新武器。这件武器本身没有立场,它的效果取决于谁在使用它、如何使用它、以及它被运用的制度环境。在公民社会强大、法治健全、信息自由传统深厚的环境中,AI工具或许能如虎添翼,助力已有的监督机制。但在公民社会孱弱、信息管控严格、权力高度集中的环境中,同样的技术更可能被用于巩固威权,或者仅仅成为一种无伤大雅的技术点缀。
最终,我们需要的或许不是对AI监督技术的盲目乐观,而是一种清醒的“技术现实主义”。这意味着,一方面要大力发展和普及这些工具,降低公民参与监督的门槛,让技术的光照进更多角落;另一方面,必须同时投入至少同等的精力,去夯实那些非技术的、根本性的政治基础:保障信息自由的法律、保护举报人的机制、独立的司法体系、活跃的公民组织和 investigative journalism 的传统。没有这些制度性“基座”的支撑,AI监督工具就像建立在流沙上的精密仪器,既无法稳定运行,也容易被轻易颠覆。技术可以放大声音,但无法创造声音;可以分析权力,但无法分配权力。当我们用AI之眼凝视国家时,我们真正应该思考的,是如何确保这双眼睛不被蒙蔽、不被操控,并且它所看到的不公,能够真正转化为变革的力量。这条路,远比训练一个模型要漫长和艰难得多。
参考来源
- 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
- 《重返未来:1999》三周年特别版本·3.7版本PV:他者的悲哀 - https://www.bilibili.com/video/BV1MPdBB8EEN
- 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