278 | Kieran Healy on the Technology of Ranking People
278 | 基兰·希利谈人们排名的技术

0:00:00.5 Sean Carroll: Hello, everyone. Welcome to the Mindscape Podcast. I'm your host, Sean Carroll. I wanted to start today's podcast by reading a paragraph from a new book called The Ordinal Society by Marion Forcad and Kieran Healy. So it goes like this. The idea of modernity has long been seen as having two contending aspects. On one side, the side of social organization, is the domain of rationalization and control. This is the modernity of bureaucracy, science, technology, and planning. It is the technocratic, sansimonian vision of a society run on rational principles and devoted to the elevation of humanity in the abstract. Here, the administrative task of modern organizations is to know and manage their subjects. On the other side, the side of the individual, is the domain of experience and expression.
0:00:00.5 肖恩·卡罗尔：大家好，欢迎收听《心灵景观》播客。我是你们的主持人肖恩·卡罗尔。我想在今天的播客开始时，读一段来自玛丽昂·福卡德和基兰·希利的新书《序数社会》的段落。内容如下：现代性的概念长期以来被视为有两个对立的方面。一方面，社会组织的方面，是理性化和控制的领域。这是官僚制、科学、技术和规划的现代性。这是一个基于理性原则、致力于提升人类抽象价值的技术官僚的、桑西蒙式的社会愿景。在这里，现代组织的行政任务是了解和管理他们的对象。另一方面，个体的方面，是经验和表达的领域。

0:00:52.6 SC: This is the modernity of the Romantics, of the full and authentic realization of the self and all its powers. Here the existential task of modern individuals is to know and create themselves. I like this paragraph because, you know, there's a little bit of historical resonance there, but also the contrast or the dilemma here is very real between the organizational systems-oriented view of the world and how that can bring about tremendous real benefits versus the romantic individual, you know, ignoring the system and going their own way. I think that for whatever reason, in the modern world, we prefer to personally identify with the romantic individual. But, you know, as we will talk about in this podcast, the modern world offers all sorts of conveniences and services that are only available to us if we kind of do agree to participate in the broader system.
这是浪漫主义者的现代性，充分而真实地实现自我及其所有能力。在这里，现代个体的存在任务是认识和创造自己。我喜欢这一段，因为你知道，这里有一点历史的共鸣，但同时，组织系统导向的世界观与浪漫个体之间的对比或困境是非常真实的，前者可以带来巨大的实际利益，而后者则是忽视系统，走自己的路。我认为，无论出于什么原因，在现代世界中，我们更倾向于个人认同浪漫个体。但是，正如我们将在本播客中讨论的，现代世界提供了各种便利和服务，只有当我们同意参与更广泛的系统时，这些便利和服务才会对我们可用。

0:01:50.8 SC: I recently noticed when you go to the Apple App Store and you want to download an app, they tell you what information the app gathers about you, your location data, your information from other websites or whatever, and where it sends it to. So in principle, you could just not download any app that collected information about you that you didn't want it to. I'm betting that in practice most people go ahead and just download the app, right? 'cause the app is useful. It's not like there's no point to it. I bet that most people use Google Maps when they want to go somewhere, even if that means Google knows where they're going. I've noticed that Google Maps will sometimes tell me where I parked my car. That's, on the one hand, a little weird that Google's keeping track of where I park my car. On the other hand, super convenient because I am often not very good at keeping track of where I parked my car.
我最近注意到，当你去苹果应用商店下载应用时，他们会告诉你这个应用收集了关于你的哪些信息，包括你的位置信息、来自其他网站的信息等等，以及这些信息发送到哪里。因此，原则上，你可以选择不下载任何收集你不想提供的信息的应用。但我敢打赌，在实际操作中，大多数人还是会下载这个应用，对吧？因为这个应用是有用的。并不是说它没有意义。我敢打赌，大多数人在想去某个地方时都会使用谷歌地图，即使这意味着谷歌知道他们要去哪里。我注意到谷歌地图有时会告诉我我把车停在哪里。一方面，这有点奇怪，因为谷歌在跟踪我停车的位置；另一方面，这非常方便，因为我通常不太擅长记住我把车停在哪里。

0:02:44.6 SC: So today's guest is Kieran Healy. He's one of the co-authors of the new book. And the idea is the ways in which the modern world not just keeps track of us, but classifies us, right? The ordinal society is one in which people are characterized and ranked in all sorts of different ways. Ranking people has been something that has been going on forever, of course, but technology has enabled it to happen at an enormous rate from very simple things like a credit score to hyper finely divided ways like what ads you get served up when you go to Amazon or Google or YouTube or what have you. And these forces are somewhat invisible, but all pervasive. And apparently they really matter to our lives. A lot of people, when you buy a new dishwasher, you buy a smart dishwasher and it sends information to the dishwasher company about how often you're washing your dishes.
今天的嘉宾是基兰·希利。他是新书的合著者之一。书中的观点是现代世界不仅跟踪我们，还对我们进行分类，对吧？序数社会是一个人们以各种不同方式被特征化和排名的社会。对人进行排名的事情当然已经存在很久了，但技术使得这一过程以极大的速度发生，从非常简单的事情，比如信用评分，到非常细致的方式，比如你在亚马逊、谷歌或 YouTube 等网站上看到的广告。这些力量在某种程度上是看不见的，但无处不在。显然，它们对我们的生活非常重要。很多人购买新洗碗机时，选择智能洗碗机，它会向洗碗机公司发送关于你洗碗频率的信息。

0:03:45.2 SC: And where do we draw the line? Where do we decide how much convenience is worthwhile versus how much individuality and romantic experience is worthwhile? I think these are questions we're gonna have to be struggling with more and more because these systems of surveillance and classification are not going away anytime soon. So let's go.
0:03:45.2 SC: 我们的界限在哪里？我们如何决定多少便利是值得的，多少个性和浪漫体验是值得的？我认为这些是我们将不得不越来越多地挣扎的问题，因为这些监视和分类的系统不会很快消失。那我们开始吧。

[music] [音乐]

0:04:27.5 SC: Kieran Healy, welcome to Mindscape Podcast.
0:04:27.5 SC: 基兰·希利，欢迎来到《心灵景观》播客。

0:04:28.6 Kieran Healy: Delighted to be here.
0:04:28.6 基兰·希利：很高兴来到这里。

0:04:29.6 SC: I think we got to start with a question you will find embarrassingly simple. What is the word ordinal mean to you? You have it in the title of your book.
我想我们得从一个你会觉得尴尬简单的问题开始。序数这个词对你来说意味着什么？你在书的标题中提到了它。

0:04:39.3 KH: Right. Yes, I know. It's funny. I've gotten kind of, when I've been talking about the book online or to other people, immediately the mathematicians and the physicists come out and I get a lot of quite abstruse jokes about what is this.
0:04:39.3 KH: 对。是的，我知道。这很有趣。当我在网上或与其他人谈论这本书时，数学家和物理学家们立刻就出现了，我收到了很多相当深奥的笑话，关于这是什么。

0:04:55.7 SC: I'm biting my tongue, yeah.
我在忍住不说话，是的。

0:05:00.5 KH: Right. What does this mean? Yeah. So in this case, it's fundamentally focused on the idea of ranking, although it's twofold, right? So first of all, and most importantly, it's the idea that... We live in a world where the pairing of kind of massive data sets with various processes, algorithmic, broadly conceived, statistical, mathematical, written in code of some kind, have made inroads into every social institution. And techniques of optimization and data collection are deployed to kind of streamline and organize processes across those institutions, a whole wide range of things. And the way they work is to take kind of information or data in computationally and spit out scores and especially rankings, orderings out the other side.
0:05:00.5 KH: 对。这个是什么意思？是的。在这种情况下，它基本上集中在排名的概念上，尽管它是双重的，对吧？首先，也是最重要的，是这样的想法……我们生活在一个将大量数据集与各种过程（广义上讲的算法、统计、数学，以某种代码编写）相结合的世界中，这些过程已经渗透到每一个社会机构中。优化和数据收集的技术被用来简化和组织这些机构中的各种流程，涵盖了广泛的内容。它们的工作方式是将信息或数据以计算的方式输入，然后输出分数，尤其是排名和排序。

0:05:54.8 KH: And so fundamentally, the idea of ordinality or an ordinal society is one that's based around and justified by the idea of kind of measurement and ranking. I will say too that there is a second piece to it a little bit, which comes, so Marion Foucault, who co-authored this book with me, is French, originally, she teaches in Berkeley now. But in French, the word for computer is ordinateur. And the reason that it is that word is when in 1956 or so, IBM launched the IBM 650, which was its first really mass-produced machine. It surprised them how much the demand for it was amongst businesses. And when IBM France came to sell it, they had to decide what to call it, what to call this class of device. The natural choice would have been calculator, which is the direct translation of computer.
0:05:54.8 KH: 因此，基本上，序数性或序数社会的概念是基于测量和排名的思想，并以此为依据。我还想说，这其中还有第二个方面，稍微有点不同，马里昂·福柯（Marion Foucault）与我共同撰写了这本书，她是法国人，现在在伯克利教书。但在法语中，计算机的词是 ordinateur。之所以使用这个词，是因为在 1956 年左右，IBM 推出了 IBM 650，这是其第一台真正大规模生产的机器。它们对企业对该机器的需求感到惊讶。当 IBM 法国公司来销售它时，他们必须决定如何称呼它，如何称呼这一类设备。自然的选择本应是计算器，这是计算机的直接翻译。

0:07:08.5 KH: But they consulted in a very French way. You have to be careful about what word things are gonna call. They consulted with this guy, Jacques Perret, who was a professor of Latin at the Sorbonne. He objected and he said, what about ordinateur? He said, it's a correctly formed word, it's in the dictionary, and its ultimate root is to do with ordination, the word ordination, like a religious sense of a God who brings order to the world. And he says, but that theological usage isn't frequent, so you could call it an ordinateur. And yeah, and so that's what IBM picked. And so the idea of kind of calculation as an ordering in the kind of just ordinary mathematical sense of first, second, third, fourth, but then also as a device that brings order to the world. That's what we have in mind.
0:07:08.5 KH: 但他们以非常法国的方式进行咨询。你必须小心他们将用什么词来称呼事物。他们咨询了这个人，雅克·佩雷，他是索邦大学的拉丁语教授。他提出异议，说，ordinateur 怎么办？他说，这是一个正确构成的词，字典里有，而且它的最终根源与“任命”有关，像是一个神以宗教的意义为世界带来秩序的“任命”一词。他说，但这种神学用法并不常见，所以你可以称它为 ordinateur。是的，所以这就是 IBM 选择的词。因此，计算的概念既是普通数学意义上的排序，第一、第二、第三、第四，同时也是一种为世界带来秩序的设备。这就是我们所想到的。

0:08:05.4 SC: I love the idea of Google consulting a Latin professor before... It's a different world.
我喜欢谷歌在之前咨询拉丁教授的想法……这是一个不同的世界。

0:08:12.4 KH: Right. Very much so. Yeah.
0:08:12.4 KH: 对，非常是这样。是的。

0:08:14.4 SC: So but of course we have ranked people and classified them all the time, I mean, you and I are academics, we live in a world where half of our time is spent deciding who's better than you know who else. So you're specifically focusing on how much better we are at it now because exactly of the computer age.
所以当然我们一直在对人进行排名和分类，我的意思是，你我都是学者，我们生活在一个世界里，花一半的时间来决定谁比谁更优秀。所以你特别关注我们现在在这方面有多么优秀，正是因为计算机时代的到来。

0:08:33.6 KH: Or how much more pervasive it is, yeah. One of the things we do want to kind of emphasize in the book is that it's not the case that like the fact of ranking and ordering people is a sort of fundamental aspect of human social organization. And we do that whenever there's differentiation, you implicitly have the chance of some sort of ordering or ranking of classes or categories of people comes out. So that is not new at all. In fact, it's kind of an endemic feature of just how human societies are organized. And then there's also a long history of devices and methods and techniques that we have for doing this that goes all the way back to the beginnings of... All the way back to double entry bookkeeping or that bring an ordering of things to businesses to sort of mundane devices like the filing cabinet or the card index in the 19th century, which really were kind of revolutionary in their way.
0:08:33.6 KH: 或者说它是多么普遍，是的。我们在书中想强调的一件事是，排名和排序人并不是人类社会组织的一个基本方面。每当有差异时，你就隐含地有机会对某种类别或人群进行排序或排名。这一点一点也不新鲜。事实上，这种现象是人类社会组织的一个内在特征。此外，我们还有一段悠久的历史，涉及到用于实现这一点的设备、方法和技术，这些可以追溯到……可以追溯到复式记账，或者将事物排序带入商业的设备，比如 19 世纪的文件柜或卡片索引，这些在某种程度上确实是革命性的。

0:09:37.3 KH: But what's new now is, and what's really sort of transformed not just in sort of scale, but also in scope over the last 50 years or so, is that the ability to do this has both become much more fine-grained and much more widespread. The scope that we can, the degree to which we can sort of apply these ideas and processes is sort of much wider, a whole range of kind of forms of social life that were just not within reach of any kind of measurement, certainly not any kind of real-time measurement, has really expanded. And then the degree of kind of granularity of that has also been transformed as well.
0:09:37.3 KH: 但现在的新变化是，在过去大约 50 年中，不仅在规模上，而且在范围上，能够做到这一点的能力变得更加细致和广泛。我们可以应用这些思想和过程的范围，实际上已经大大扩大，涵盖了许多以前无法测量的社会生活形式，当然也没有任何实时测量的可能性。而且，这种细致程度也发生了变化。

0:10:24.8 SC: I will mention for those of you who are just listening over audio, that Kieran in the background has an original Apple Macintosh computer here. And is it a working model?
0:10:24.8 SC: 我想提一下，对于那些只是听音频的人，背景中的基兰有一台原版的苹果麦金塔电脑。它是一个可以正常工作的型号吗？

0:10:35.6 KH: Oh, absolutely. It works. Yeah.
0:10:35.6 KH: 哦，绝对可以。它有效。是的。

0:10:38.1 SC: So when we talk about the history here, you know, you and I both lived through a lot of it anyway and you know what I'm talking about.
所以当我们谈论这里的历史时，你知道，你我都经历了很多，反正你知道我在说什么。

0:10:42.2 KH: Yeah, yeah. No, we're in that sort of, I think, just about in that intermediate generation that is cursed to explain computers both to people older than us and people younger than us.
是的，是的。我们正处于那种，我认为，正好在那个中间代，注定要向比我们年长和年幼的人解释计算机。

0:10:54.1 SC: There are worse curses than that. But just so the audience gets in mind what we're talking about here, it's not just rankings that you're concerned with in the book. I mean, it's just the very, it's kind of a classification ability as well.
有比这更糟糕的诅咒。但为了让观众明白我们在谈论什么，书中关注的不仅仅是排名。我是说，这实际上也是一种分类能力。

0:11:08.9 KH: Yeah. Yeah. So, and those two things are very kind of closely connected, right? That there's two processes to, before you can rank things, you must name them first, right? And so there's, so there's a process of classification that, that takes place, which is sort of identifying categories and deciding which things fall into which categories. So the sort of nominalizing thing that this is an instance of that, this other thing is an instance of the second thing. So that's sort of, you know, so as we say in the book, machines classify because people do in the same ways and they rank because people do. But there's always this kind of, there's always a very strong tendency then for nominal classifications, which should be unordered or which are often unordered to turn into rankings and then positioning people within those things.
0:11:08.9 KH: 是的。是的。所以，这两件事是非常紧密相连的，对吧？在你能够对事物进行排序之前，必须先给它们命名，对吧？所以有一个分类的过程，这个过程是识别类别并决定哪些事物属于哪些类别。因此，这种名词化的过程就是这个事物是第二个事物的一个实例。所以，这就是我们在书中所说的，机器进行分类是因为人们以相同的方式进行分类，它们进行排序也是因为人们这样做。但总是有一种非常强烈的倾向，使得本应无序的名义分类，往往会转变为排序，然后在这些事物中对人进行定位。

0:12:07.2 SC: Plato famously said that one of the jobs of philosophy is to carve nature at its joints, right? So we're handing that job over to our computers now in some way.
0:12:07.2 SC: 柏拉图 famously 说过，哲学的一个任务是沿着自然的关节切割，对吧？所以我们现在在某种程度上把这个任务交给了我们的计算机。

0:12:17.6 KH: Yeah, I mean, to a large degree. And certainly kind of the... One of the... We're handing it over to them and they're extremely powerful and fast. And the legitimacy that we invest or that these systems often come to have derives in part from this idea that they are in fact, that this is a real, that you're really picking up on something real in the world and that you're doing it with a degree of precision and accuracy that hasn't been possible in the past. And of course, we know, like anybody who's worked with data of any kind in a quantitative form, is gonna be well aware of just how difficult it is to kind of cleanly collect information that truthfully reflects the way things are. And then with social data in particular, there's all kinds of additional complexities about the degree to which you're imposing your framework.
0:12:17.6 KH: 是的，我的意思是，在很大程度上。并且当然，这种... 我们把它交给他们，他们非常强大和快速。我们所投入的合法性，或者说这些系统通常所拥有的合法性，部分源于这样一个观念：它们实际上是，这确实是你在真实世界中捕捉到的某种真实事物，并且你以一种在过去无法实现的精确度和准确性来做到这一点。当然，我们知道，任何与定量形式的数据打过交道的人都非常清楚，干净地收集真实反映事物状态的信息是多么困难。尤其是对于社会数据，还有各种额外的复杂性，涉及到你在多大程度上施加了自己的框架。

0:13:21.3 KH: When I'm in talks with this stuff, I do have, to go back to the carving nature at the joints thing, I sometimes refer to that idea and I have a little slide from an American cookbook that I have and a French one showing the very different cuts of beef that exist in different, in France and the United States. And so, even butchers carve nature at the joints quite differently. So there's heterogeneity, even if ultimately there's still a cow under there. There are different ways to cut it up.
0:13:21.3 KH：当我谈论这些东西时，我确实有时会提到关节处雕刻自然的想法，我有一张来自我拥有的美国食谱的小幻灯片和一张法国食谱，展示了在法国和美国存在的非常不同的牛肉切割方式。因此，即使是屠夫在关节处雕刻自然的方式也大相径庭。所以即使最终下面仍然是一头牛，但切割的方式是不同的。

0:13:53.1 SC: I don't know if you noticed just today, and I think it was in Vox, they had an analysis of the US House of Representatives. And to your point of the data are hard to sometimes get, and therefore you can get better results. But also we have the categories that we testified to, that we claim we're members of, and then we have the categories that are actually borne out by our actions, right? So in this article, they went through all the voting patterns of different members of Congress and grouped them into eight groups on the basis of affinity of that voting. So on the flip side, you can actually reveal categories that were there all along, but we didn't mention them, yeah?
0:13:53.1 SC: 我不知道你今天是否注意到了，我认为是在 Vox 上，他们对美国众议院进行了分析。关于你提到的数据有时很难获取，因此你可以获得更好的结果。但我们也有我们所宣称的类别，我们声称自己是这些类别的成员，然后我们还有实际上通过我们的行为体现出来的类别，对吧？所以在这篇文章中，他们分析了不同国会议员的投票模式，并根据投票的亲和力将他们分为八个组。因此，从另一个角度来看，你实际上可以揭示那些一直存在但我们没有提到的类别，是吗？

0:14:37.5 KH: Yeah, and that's one of the main sources of the kind of power and legitimacy of these kinds of classifications and methods of data collection and analysis. Because like I say, for the longest time, organizations of all kinds, whether it's businesses or in the longer term, the state, have been very interested in discovering information about either the customers that they have or the citizens that make up or the people whom they govern. But it turns out, for a long time, it was extremely difficult to do this at any kind of reasonable speed. You have a census every 10 years, or maybe you collect sample surveys. Indeed, the idea of statistics, the word stat in there, is closely related to the state. It's information about a population.
0:14:37.5 KH: 是的，这正是这些分类和数据收集与分析方法的权力和合法性的主要来源之一。因为正如我所说，长期以来，各种组织，无论是企业还是更长期的国家，都非常希望发现有关他们拥有的客户或他们所治理的公民的信息。但事实证明，长期以来，以任何合理的速度做到这一点都是极其困难的。每十年进行一次人口普查，或者可能收集样本调查。实际上，统计学这个词中的“stat”与国家密切相关。这是关于一个人口的信息。

0:15:31.8 KH: The promise of these new methods is that with the expansion of all of these methods of data collection, initially kind of on the desks and then ultimately in your pocket of... You're getting a record increasingly of individual and social activity that isn't just dependent to the promises. It isn't just about kind of asking people what it is they think or what it is that they... How it is that they would want to be classified, you also get this essentially, the promise of essentially behavioral data about people, which you want to say is kind of more truthful. And so that leads immediately to this idea that there's a, you know, at the level of the classifiers, a lot of legitimacy associated with, look, I'm capturing what you're actually doing.
0:15:31.8 KH：这些新方法的承诺在于，随着所有这些数据收集方法的扩展，最初是在桌面上，最终是在你口袋里的……你越来越能够记录个人和社会活动，这不仅仅依赖于承诺。这不仅仅是询问人们他们的想法或他们希望如何被分类，你还获得了关于人们的行为数据，这可以说更真实。因此，这立即引出了这样一个想法，即在分类器的层面上，与“看，我正在捕捉你实际所做的事情”相关联的合法性很高。

0:16:31.7 KH: If I ask you how many steps you walked today, you might tell me one number, maybe you're bad at guessing it, or maybe you prefer to think it was a little higher than it is, but your watch on your wrist knows. And so that behavioral data is both very valuable and it does kind of capture something that's much more accurate in some sense about what people are actually doing, but then it also provides this kind of tremendous legitimacy to the classifications that you develop from it and it introduces the possibility of saying, well, the things that flow from you being in one of these categories are really your own fault or your own virtue, depending on what... If you're well-classified, you experience it as being kind of a virtuous sort of sense of that you're correctly classified. And if you're in a sort of poor... If you're in a poorer category, the idea is that then you're to blame for your own social situation.
如果我问你今天走了多少步，你可能会告诉我一个数字，也许你不擅长猜测，或者你更喜欢认为这个数字比实际的要高一点，但你手腕上的手表知道。因此，这种行为数据既非常有价值，又在某种意义上更准确地捕捉到人们实际在做什么，但它也为你从中发展出的分类提供了巨大的合法性，并引入了这样一种可能性：那么，来自于你处于这些类别中的事情，实际上是你自己的过错或美德，这取决于... 如果你被正确分类，你会体验到一种美德的感觉，觉得自己被正确分类。如果你处于一个较差的类别，那么这个想法就是你要为自己的社会状况负责。

0:17:27.0 SC: Well, there's definitely an idea lurking in the background of the book. I mean, maybe you say it very explicitly and I just sort of glossed over, but we have an idea in the modern liberal world that we forge ourselves, right? That we create who we are and we have an image of ourselves and maybe we don't always live up to it, but okay, we're trying, good for us. And what you're driving home is that every corporation, Amazon and Apple and Facebook and whatever, has an image of us. Like they know who we are in maybe a very different way. And it's disconcerting to think both that they have that and that it's not who we think we are.
书中确实潜藏着一个想法。我的意思是，也许你说得很明确，而我只是略过了，但我们在现代自由世界中有一个观念，那就是我们自己塑造自己，对吧？我们创造了自己的形象，也许我们并不总是能达到这个形象，但好吧，我们在努力，值得我们为此感到高兴。而你所强调的是，每个公司，亚马逊、苹果、脸书等等，都有一个关于我们的形象。他们以可能非常不同的方式了解我们。想到他们拥有这样的信息，并且这并不是我们认为的自己，这让人感到不安。

0:18:07.7 KH: Yeah. And then there's that point where those two things coincide, right? And so yes, like one distinctive feature of modernity, of the existing in the world kind of that we're in now is the idea that people are... That individuals are kind of autonomous agents with their own preferences and rights, who make their own decisions in the world, who are going about sort of choosing to do things and are kind of empowered, active, agentic is the word often that sociologists will use, imbued with this kind of sense of a busy little person going around and making their own choices. And then on the other hand, you have this idea of kind of this sea of recorded information about us yields this sort of set of digital traces that produce this kind of shadow of ourselves, a data double of ourselves, if you like. That's a phrase that Dan Book, as a historian coined, that kind of represents us truthfully in some sense.
0:18:07.7 KH: 是的。然后有一个点，这两件事重合，对吧？所以是的，现代性的一个显著特征，就是我们现在所处的世界中，人们……个体是自主的代理人，拥有自己的偏好和权利，他们在世界中做出自己的决定，选择去做事情，感到被赋权，积极主动，社会学家常用“代理性”这个词，充满了那种忙碌的小人物四处走动并做出自己选择的感觉。另一方面，你有这样一个观点，即关于我们的记录信息的海洋产生了一系列数字痕迹，形成了我们自己的影子，一个数据双胞胎，如果你愿意的话。这是历史学家丹·布克创造的一个短语，在某种意义上真实地代表了我们。

0:19:22.0 KH: And that kind of organizations can know about us. And so our sense of agency, our sense of being a kind of active person in the world is very closely bound up with our feelings of authenticity, like of being true to ourselves, this kind of romantic conception of being true to ourselves. But then the measurement and the representations in numbers and records that exist of us is very closely connected to sort of our need to be authenticated formally by... And those two processes coincide. The world we're in, on the one hand, you're enjoined to be really yourself in the authenticity sense, but then also organizations are very concerned to know whether you're really who you say you are. That is, in the sense of it's necessary that you be authenticated as you, as an identity, rather have somebody impersonating you or, and so on.
0:19:22.0 KH: 这种组织可以了解我们。因此，我们的能动感，我们作为世界上一个积极的人的感觉与我们的真实性感受紧密相连，就像忠于自我的一种浪漫观念。但是，关于我们的测量和以数字和记录的形式存在的表现与我们被正式认证的需求密切相关……这两个过程是重合的。在我们所处的世界中，一方面，你被要求在真实性的意义上做真实的自己，但另一方面，组织也非常关心你是否真的是你所说的那样。也就是说，必须确认你作为一个身份的真实性，而不是让其他人冒充你，等等。

0:20:23.2 SC: And maybe the scale of this kind of operation makes it hard for people to visualize what's going on. I mean, if I worked at Amazon, I mean, clearly somewhere buried in the bowels of a data center is some list of correlations of all the things I've ever purchased or shopped for or whatever. But could a person working at Amazon sit at a terminal and call up a profile of somebody? It seems impractical. There's a lot of customers out there.
0:20:23.2 SC: 也许这种操作的规模让人们很难想象发生了什么。我是说，如果我在亚马逊工作，显然在数据中心的某个深处有一份我曾经购买或浏览过的所有物品的相关列表。但是，在亚马逊工作的人能否坐在终端上调出某个人的资料？这似乎不太实际。外面有很多客户。

0:20:52.3 KH: There are a lot of customers out there, yeah. Ultimately, they probably could. Somebody can. It is true though, that it's not a question. Most of the time, organizations are not specifically interested in you or me. We're not particularly interesting or important enough, which is one of the reasons, perhaps, that your Amazon recommendations might be weird or governed by other things. One promise of all of this stuff is that, again, speaking to the language of personal authenticity and tailoring, that everything could be personalized to you and that the recommendations that Sean Carroll gets on Amazon would just be perfect. But then it's very common for us to have this experience of saying, oh, I've been an Amazon customer or a customer of some other similar organization for a couple of decades and they recommend these things to me and I don't know why I get them.
有很多客户在外面，是的。最终，他们可能可以。有人可以。确实，问题并不是大多数时候，组织并不特别关注你或我。我们并不是特别有趣或重要，这可能是你在亚马逊上的推荐可能会奇怪或受其他因素影响的原因之一。所有这些东西的一个承诺是，再次谈到个人真实性和定制的语言，一切都可以个性化到你身上，肖恩·卡罗尔在亚马逊上得到的推荐将是完美的。但我们常常有这样的体验，哦，我已经是亚马逊的客户或其他类似组织的客户几十年了，他们向我推荐这些东西，我不知道为什么我会得到它们。

0:21:47.1 SC: It's still terrible.
0:21:47.1 SC: 这仍然很糟糕。

0:21:47.2 KH: But again, this is the actual kind of the demands of doing this practically are quite strong. Organizations know and very strongly feel that they should be collecting this kind of level of granular data about you. And they will boast sort of internally or they will organize themselves. We call this kind of the data imperative. They know that this is something they should be doing. And there's a whole infrastructure, there's a whole set of occupations of people who tend data lakes and who manage data infrastructures about individuals. And as Maciej Czajkowski has said, the whole imagery of that, the metaphorical imagery of it is like a kind of accident waiting to happen.
0:21:47.2 KH: 但再说一次，实际上进行这项工作的要求非常强烈。组织知道并且非常强烈地感觉到，他们应该收集关于你的这种细粒度数据。他们会在内部自夸，或者会组织自己。我们称之为数据的必要性。他们知道这是他们应该做的事情。整个基础设施，整个职业群体都在管理数据湖和个人数据基础设施。正如 Maciej Czajkowski 所说，这一切的意象，隐喻的意象就像是一场即将发生的事故。

0:22:34.4 KH: This data lake is dammed up behind a barrier that could crack at any time or be overflowed and get released into the world. So on the one hand, it's very difficult in practice to get that kind of granularity and ease of access to data, about a particular person have it be useful. On the other hand, there are the leading edge of this are the best institutionalized versions of these scores really do exist and are used all the time.
这个数据湖被一个可能随时破裂或被淹没并释放到世界的障碍物所阻挡。因此，一方面，在实践中获得这种粒度和对特定个人数据的便捷访问非常困难，且使其有用。另一方面，最前沿的这些评分的最佳制度化版本确实存在，并且一直在使用。

0:23:04.1 KH: The credit score is the most obvious one where you have, in the United States, where you have exactly that kind of a single number that characterizes your behavior in a way that you are sort of morally responsible for, that reflects your actual behavior when it comes to paying your debts or not, and that any shop assistant can call up and decide whether to make you a store credit offer or something like that, and whose initial usage in relatively restricted circumstances has blossomed out into kind of much like the driver's license becomes an effectively a national identity card, a credit score becomes the gateway to having a harder or easier time in areas where it was never really initially designed to be applied at all.
信用评分是最明显的例子，在美国，你有一个确切的数字来表征你的行为，这在某种程度上是你道德上负责的，反映了你在偿还债务方面的实际行为，任何店员都可以调出这个数字并决定是否给你提供商店信用额度之类的东西，而它最初在相对有限的情况下使用，后来发展成类似于驾驶执照，实际上成为了一种国家身份证，信用评分成为了在一些最初并不打算应用的领域中，获得更容易或更困难的通行证。

0:23:55.5 SC: Well, let's... Yeah, this says many juicy things to talk about here that we've leapt ahead. But you do open the book with some history and it's fascinating. In the halcyon early days of computers and the internet and so forth, the expectations of where these capacities would go were very different than where they ended up ending up.
好吧，让我们……是的，这里有很多有趣的事情可以讨论，我们已经提前跳过了。但你确实在书的开头提到了一些历史，这非常吸引人。在计算机和互联网的美好早期，关于这些能力将走向何方的期望与最终的结果截然不同。

0:24:20.9 KH: Yes, very much so. I mean, there's this period beginning... The prehistory of this in the 1960s and '70s when computing is, as we know it, is kind of becoming established and just getting off the ground, is this strange fusion on the one hand of kind of what we think of as the more Dr. Strangelove almost elements of computers come out of the war, of code breaking, of defense systems, and command and control methods for missiles, and all of that kind of stuff. And at the same time, really from the beginning, you also have this kind of like hacker culture amongst engineers who want to tinker and experiment and mess around with. In that sort of familiar kind of scientifically sort of, let's push this and see where it can go, that's quite flat and sort of libertarian in its way, where people just want to be left alone and blue sky research type stuff, just want to be left alone and do their thing.
0:24:20.9 KH: 是的，非常如此。我的意思是，从 1960 年代和 70 年代开始，这个时期的前史，当计算机如我们所知的那样逐渐建立并刚刚起步时，出现了一种奇怪的融合，一方面是我们认为的更像《奇爱博士》中的计算机元素，这些元素源于战争、破译密码、防御系统以及导弹的指挥和控制方法等等。与此同时，实际上从一开始，你也有这种工程师之间的黑客文化，他们想要修补、实验和玩弄。在那种熟悉的科学氛围中，大家想要推动事物的发展，看看能走到哪里，这种文化相对平坦，带有某种自由主义色彩，人们只想被独立对待，进行蓝天研究类型的工作，只想被留下来，做他们的事情。

0:25:22.7 KH: And as computers take their kind of modern form through the hobbyist era of the 1970s into their kind of expansion into business and society at large in the 1980s and 1990s, those two tendencies kind of continue to coexist. And so by the '90s, when the internet and the World Wide Web in particular is developed and becomes widely available as a protocol on the web. There is this kind of, that's the sort of high watermark in a lot of ways of excitement about the kind of pure freedom associated with just setting out on your own, setting up your website, these homestead dreams, we call them, and that language of kind of digital homesteading, Howard Rheingold used that term at the time.
0:25:22.7 KH：随着计算机在 1970 年代的爱好者时代逐渐形成现代形态，并在 1980 年代和 1990 年代扩展到商业和社会，这两种趋势继续共存。因此到了 90 年代，当互联网，特别是万维网被开发并作为一种协议广泛可用时，这在很多方面成为了与单独出发、建立自己的网站相关的纯粹自由的兴奋感的高峰。我们称之为这些自给自足的梦想，霍华德·莱因戈尔德在当时使用了“数字自给自足”的这个术语。

0:26:13.1 KH: People just kind of, this is a place where we can be free of all of the, in effect, all of the world of ranking and of the world of local status and of the suffocating kind of, what John Perry Barlow calls the weary giants of flesh and steel, right? They had this image of cyberspace sort of being a kind of free for all, a new frontier where the dead hand of kind of post-war suburban industrial society would no longer touch you. And you could just be yourself. Again, the romantic image of a homesteader empowered by technology. And that was really kind of the beginning of... Yeah, that's where we started a very different kind of set of associations having to do with what this new networking technology, what these new protocols would enable.
人们在这里可以摆脱所有的排名、地方地位以及约翰·佩里·巴洛所称的肉体与钢铁的疲惫巨人所带来的窒息感。这种网络空间的形象就像是一个自由的天地，一个新的边疆，战后郊区工业社会的死手将不再触及你。你可以做你自己。这是一个被技术赋能的开拓者的浪漫形象。这实际上是一个非常不同的联想开始的地方，涉及到这项新网络技术和这些新协议所能实现的内容。

0:27:07.3 SC: Well, and it's very common that people trying to predict what the impact of a new technology is going to be, get it wildly wrong. And maybe it's just because of wishful thinking or whatever. But looking back on examples of this, and with some sociological wisdom in the background, you know, are there systematic ways that people get these futuristic scenarios wrong? Or should we be better at predicting? Is there some equilibrium we're always gonna go to?
0:27:07.3 SC: 好吧，人们在预测新技术的影响时，常常会大错特错。这可能只是出于一厢情愿的想法或其他原因。但回顾这些例子，并结合一些社会学的智慧，我们是否可以发现人们在这些未来场景中系统性地出错的方式？或者我们应该更擅长预测吗？是否存在某种我们总会回归的平衡？

0:27:37.3 KH: Yeah, I mean, as the kind of conventionalism goes correctly. Nothing defines an era better than its vision of the future. And there are moments when... This sense of kind of a set of possibilities that existed and then were in some sense closed off or that's not how things turned out. It's extremely common.
0:27:37.3 KH: 是的，我的意思是，按照传统主义的说法，没有什么比一个时代对未来的展望更能定义这个时代了。有些时刻……这种存在的一系列可能性在某种意义上被封闭，或者事情并没有朝着那个方向发展，这种情况是非常常见的。

0:28:04.1 SC: It's a common story.
这是一个常见的故事。

0:28:05.8 KH: It is a very common story. And the main thing that happens, I suppose, is people project their own desires about what an ideal world would be onto whatever the sort of social change, often a technological change, but not always, you know, what that seems to enable. And then as it sort of, as it goes on, and things don't quite work out that way, one of the things that can happen is that the initial, especially for the sort of utopian visions of things, there can be a kind of immense disappointment amongst the utopian vanguard with everyone else and their failure.
这是一个非常常见的故事。我想，主要发生的事情是，人们将自己对理想世界的渴望投射到任何形式的社会变革上，通常是技术变革，但并不总是如此，您知道，这似乎使得这一切成为可能。然后，随着事情的发展，情况并没有完全按照预期的方式进行，其中一个可能发生的事情是，尤其对于那些乌托邦式的愿景，乌托邦先锋与其他人之间可能会产生一种巨大的失望，因他们的失败。

0:28:44.5 SC: The rest of the world let us down.
世界其他地方让我们失望。

0:28:51.2 KH: Yeah, I think that's a very common feature. Sort of revolutionaries end up with kind of a disappointment verging on contempt for the peasants that they have liberated. And one of the things that happened in the development of the World Wide Web was this transition from the kind of homestead era, people sort of said, well, this is great. I love being online. I love talking to my friends. I love being able to be in touch with people who are like me. I didn't know there were often people just like me and lots of them that I can... That really was a kind of liberating feature of these kinds of technologies. However, I don't want to run my own website or I would rather not administer my own servers or could you just... I need to find people more quickly and more effectively, can someone take care of that for me?
0:28:51.2 KH: 是的，我认为这是一种非常普遍的特征。革命者最终对他们所解放的农民感到失望，甚至有些轻蔑。在互联网发展的过程中，发生了一种转变，从那种自给自足的时代，人们开始说，这太好了。我喜欢在线上。我喜欢和朋友聊天。我喜欢能够与和我一样的人保持联系。我不知道有很多人和我一样，而且有很多人可以... 这确实是这些技术的一种解放特征。然而，我不想自己运营网站，或者我宁愿不管理自己的服务器，或者你能不能... 我需要更快更有效地找到人，有人能为我处理这些吗？

0:29:41.9 KH: And there is a sort of tendency to think, and this is something that's out in the world, but it's also kind of a feature of kind of social criticism and theorizing about kind of the internet. There is this tendency to think that the kind of much more suburbanized, centralized, perhaps hierarchical internet that we ended up with was imposed on people very much against their will. It was certainly imposed on some people against their will, the original homesteaders. But a lot of it was very much kind of demand driven where people kind of preferred the convenience of somebody else taking care of these things for them in order to get what they wanted, which was often the kind of sheer sociability, but not all of the associated kind of system administration.
0:29:41.9 KH：人们有一种倾向，认为这是一种存在于世界上的现象，但这也是一种社会批评和关于互联网理论的特征。人们倾向于认为，我们最终得到的这种更加郊区化、集中化，或许是等级化的互联网，是在很大程度上违背人们意愿的情况下强加给他们的。确实，有些人是被强加的，尤其是最初的拓荒者。但很多时候，这种情况实际上是由需求驱动的，人们更倾向于选择让别人为他们处理这些事情，以便获得他们想要的东西，这通常是单纯的社交性，而不是所有相关的系统管理。

0:30:28.0 KH: And they would prefer people to take care of that. And so it is, it's tempting to think that, oh, we could have had nice things, but then the corporations came along and sort of made this world terrible for us. Now, it's not that there's nothing to that critique, but it is the case that people do want different things. And one of the things people really wanted was convenience and the ability to just get to the kind of fun social part, which in part led to the concentration of infrastructure that we kind of now have with a small number of companies and platforms facilitating just that sort of thing. And there were many cases where companies try to impose their way of doing things on an individual and failed. And we have a lot of failed giants in the first dot-com era and afterwards. So it's not that people are duped, but it's a more complicated process because on the one hand, companies are trying to guess what people want. But on the other hand, people then always tend to kind of overflow or do things with technologies that the people seeking to kind of run them don't expect a lot of the time.
0:30:28.0 KH: 他们更希望人们来处理这些事情。因此，想当然地认为，我们本可以拥有美好的事物，但随后公司出现，使这个世界变得糟糕，这种想法是很诱人的。虽然这种批评并非毫无道理，但人们确实想要不同的东西。而人们真正想要的其中一件事就是便利，以及能够直接进入那种有趣的社交部分，这在一定程度上导致了基础设施的集中，现在我们有少数公司和平台来促进这种事情。还有很多案例表明，公司试图将自己的做事方式强加给个人，但失败了。在第一次互联网泡沫时代及其后，我们有很多失败的巨头。因此，并不是说人们被愚弄，而是这是一个更复杂的过程，因为一方面，公司试图猜测人们想要什么；另一方面，人们总是倾向于以公司管理者意想不到的方式使用技术。

0:31:46.8 SC: Well, clearly, we're at another moment right now with LLMs and artificial intelligence, where there's a new set of utopians coming in to promise us things. And so I'm just trying to figure out, like, how do we avoid making the same mistakes again? Clearly, one feature of humanity is that there will be a bunch of rapacious capitalists, or whatever the version of people who want to accrue power to themselves. And on the other hand, there's going to be a large number of people who will vote for convenience over freedom every time. And so does that help us guess what kind of future AI will bring about?
0:31:46.8 SC: 很明显，我们现在正处于一个新的时刻，关于LLMs和人工智能，出现了一批新的乌托邦主义者来向我们承诺各种事情。因此，我只是想弄清楚，我们如何才能避免再次犯同样的错误？显然，人性的一个特征是会有一群贪婪的资本家，或者说那些想要为自己积累权力的人。另一方面，会有大量的人每次都选择便利而非自由。那么，这是否有助于我们猜测人工智能将带来什么样的未来？

0:32:28.6 KH: I think that there's a lot of similarities to, I mean, OpenAI is its own, I don't know, OpenAI, the specific company, but the large language models and artificial intelligence generally is its own sort of technology with its own distinctive features. And so things don't happen exactly the same way. But the way that things are rolling out with a lot of this stuff is quite similar to things that have happened before. And so we get sort of, one of the main ways this tends to happen is that this transition from a world of initial technology with seemingly infinite possibilities to one where there's a smaller group where those possibilities seem to narrow and then we're stuck with things that we have difficulties with, is that the... So the initial technology really is amazing and delightful and astonishing to people.
我认为有很多相似之处，虽然 OpenAI 是一个独特的公司，但大型语言模型和人工智能一般来说是一种独特的技术，具有其独特的特征。因此，事情的发展并不完全相同。但许多新事物的推出方式与之前发生的事情非常相似。因此，这种情况的主要表现之一是，从一个初始技术的世界，似乎有无限的可能性，过渡到一个可能性似乎缩小的小团体，然后我们被困在一些我们难以处理的事情中。初始技术确实让人惊叹、愉悦和震惊。

0:33:38.9 KH: And I think, again, this is sort of something that's easy to underplay if you're a cranky social critic who's sort of just sick of that... We start with somebody... Someone our age might think of the first time that they used the World Wide Web or first time that they saw a webpage load. Personally in my case, it was as an undergraduate in my friend Owen's physics lab. He was a master's student and they had a deck alpha running NCSA Mosaic and we downloaded pictures of Mars from the JPL. We didn't have any reason... I didn't have any reason to. I was a social science student. I didn't have any reason to. I didn't need pictures of Mars. But just the fact that you could do it, that there was this, you could talk to this computer in Pasadena and it would serve up these things to you.
0:33:38.9 KH: 我认为，这种事情如果你是一个脾气暴躁的社会评论家，可能会被轻视……我们从某个人开始……我们这个年龄的人可能会想到他们第一次使用万维网或第一次看到网页加载的情景。就我个人而言，那是在我作为本科生时，在我朋友欧文的物理实验室里。他是一个硕士生，他们有一台运行 NCSA Mosaic 的 Deck Alpha，我们从 JPL 下载了火星的图片。我们没有任何理由……我没有任何理由去做。我是一个社会科学专业的学生。我没有理由去做。我不需要火星的图片。但仅仅是因为你可以做到这一点，能够与位于帕萨迪纳的这台计算机对话，它就能为你提供这些东西。

0:34:31.7 KH: That was incredible. And then similarly, a decade later, you have a phone in your pocket that can render a map of your current location and show you things around. And you're just holding it. That's amazing. And a decade after that. You take a descendant of that phone out of your pocket and you touch some buttons and you can summon a car to take you wherever you wanna go. And now it's kind of like, oh, you take that same device out of your pocket and you ask it a question and it speaks, or it can generate sort of text. So I don't underestimate at all or discount that degree of kind of delight. Something else will be this... In another 10 years, something else will have that effect on us. Now, how do those things kind of play out?
0:34:31.7 KH: 那真是不可思议。十年后，你口袋里有一部手机，可以渲染你当前位置的地图，并显示周围的事物。你只是拿着它。这太神奇了。再过十年，你从口袋里拿出那部手机的后代，按几个按钮，就可以召唤一辆车带你去任何你想去的地方。现在有点像，你拿出同样的设备，问它一个问题，它会说话，或者可以生成一些文本。所以我一点也不低估或忽视这种愉悦感。还有其他的……再过十年，其他东西也会对我们产生这样的影响。那么，这些事情将如何发展呢？

0:35:18.4 KH: How does that moment become kind of the infrastructure that we end up with, whether it's the web or everybody in the world who can afford a smartphone owning one, or the platformized world of labor for Uber drivers, with all its exploitative dimensions and undertones and entrenched ratings, and now again with kind of artificial intelligence? Well, the first thing that happens is that we get given this for free as a gift, so to speak.
0:35:18.4 KH：那一刻如何成为我们最终所拥有的基础设施，无论是网络，还是全世界能够负担得起智能手机的人都拥有一部，或者是 Uber 司机的劳动平台化世界，伴随着所有剥削性的维度和潜在的暗示以及根深蒂固的评级，现在又与人工智能有关？首先发生的事情是，我们可以把这视为一种免费的礼物。

0:35:45.4 SC: The first is free.
第一个是免费的。

0:35:46.4 KH: It's given away to us. And so one of the reasons that it's delightful is that it's sort of given to us as a... And as any sociologist or anthropologist will tell you, gifts set up these expectations of a return. What we give in return is information about ourselves. And we give information about ourselves, our location, or behavior, or who we're interacting with, what we want to know, the questions that we ask, and so on. And it's from there then that these organizations then seek to take that information and make it profitable or take that knowledge and make it profitable. And at the beginning, with the web especially, that first stage with things like Google Search, that was a real revelation. It took a while for people to figure that out.
0:35:46.4 KH：这是送给我们的。因此，它令人愉悦的原因之一是，它在某种程度上是作为一种礼物送给我们的。正如任何社会学家或人类学家会告诉你的，礼物会建立起回报的期望。我们回报的是什么呢？是关于我们自己的信息。我们提供关于我们自己的信息、我们的位置信息、我们的行为、我们与谁互动、我们想知道什么、我们提出的问题等等。然后，这些组织就会寻求利用这些信息来获利，或者利用这些知识来获利。在一开始，尤其是在网络时代，像谷歌搜索这样的第一阶段，确实是一个真正的启示。人们花了一段时间才弄明白这一点。

0:36:33.8 KH: That the digital traces and the logs left behind was actually kind of potentially tremendously valuable. And so with OpenAI now and similar companies, the world of large language models, yeah, the question is kind of... I would expect the same sort of... The thing that tends to keep happening is the same sort of concentration of service provision amongst... That you just get a couple of competitors, really not, maybe not directly competing, but we saw that with smartphones, like with... You have Apple and the Google Android platform, and that's kind of it. And we see it with these other platforms as well. The thing that's distinctive about the world of artificial intelligence, or one of the things that's distinctive about its most widespread use cases is that they're now kind of... Having trained themselves on the free gift of everything that is the World Wide Web and all its content that was available.
数字痕迹和留下的日志实际上可能是非常有价值的。因此，随着 OpenAI 和类似公司的出现，大型语言模型的世界，问题在于...我会期待同样的...往往发生的事情是服务提供的集中化...你只会得到几个竞争者，实际上可能并不是直接竞争，但我们在智能手机上看到了这一点，比如...你有苹果和谷歌安卓平台，就差不多了。我们在其他平台上也看到了这一点。人工智能世界的一个独特之处，或者说其最广泛应用案例的一个独特之处在于，它们现在有点...已经在互联网上的所有内容这一免费礼物上进行了自我训练。

0:37:45.0 KH: Now they're in the process of kind of emitting the effluvia of AI-generated output back into that environment. And it's not clear to me what's going to happen. Because again, one of the things that happened with a lot of the sort of the first 20 years of social activity on the web has gradually declined in terms of its kind of public accessibility. And there's still just as much social activity, more than ever really, taking place, broadly speaking, online. But much of it has retreated either to platforms where you can't see, unless you're, or whether it's with things like messaging or content. But down to thing as mundane as having a substack rather than a website, being in a Discord or a Slack rather than in a web forum or blog comments and so on. And so if all that's left in public is the sort of slop of AI output, that might pose problems for this technology in the future.
0:37:45.0 KH：现在他们正在将 AI 生成的输出的废物重新释放到那个环境中。我不清楚会发生什么。因为再次提到，网络上前 20 年的社交活动逐渐在公共可及性方面下降。总体而言，在线上仍然有同样多的社交活动，实际上比以往任何时候都要多。但其中许多活动已经退回到一些平台上，除非你在其中，否则你无法看到，或者是通过消息传递或内容等方式。但也有一些平凡的事情，比如拥有一个 Substack 而不是一个网站，或者在 Discord 或 Slack 中而不是在网络论坛或博客评论中。因此，如果公共领域中只剩下 AI 输出的杂乱，那可能会给这项技术的未来带来问题。

0:38:48.7 SC: Maybe, I think we skipped ahead a lot to talk about this data collection and its implications just because we all know that our data is being collected. We've all seen Amazon serve up its recommendations. But you've thought about this a lot more carefully than most of us. I mean, what is your overview of the ways in which the data is being collected? Some of them are obvious, but probably some of them are less so to the people who aren't thinking about it all the time.
0:38:48.7 SC: 也许，我认为我们跳过了很多内容来讨论这个数据收集及其影响，因为我们都知道我们的数据正在被收集。我们都见过亚马逊提供的推荐。但你对此的思考比我们大多数人都要深入。我是说，你对数据收集方式的总体看法是什么？有些方式显而易见，但对那些不经常思考这个问题的人来说，可能有些方式就不那么明显。

0:39:16.3 KH: Yeah, there's a couple of different dimensions to this. Like the... What's happening? There's such a volume of data that's available to companies now just because of the gradual expansion of kind of all of these ways for monitoring and tracking individuals. That's often kind of presented just in terms of surveillance, let's say, and that it's just people spying on you. But I think that sort of tends to underestimate the degree to which kind of social life in general as a whole is taking place in these environments where you're being kind of monitored.
0:39:16.3 KH: 是的，这个问题有几个不同的层面。发生了什么呢？现在公司可以获得大量的数据，这主要是因为各种监控和追踪个人的方式逐渐扩展。通常这被简单地呈现为监视，比如说，就是有人在监视你。但我认为这往往低估了社会生活整体上在这些被监控的环境中进行的程度。

0:39:57.9 KH: So it's not quite that kind of, it's not like street cameras, although that's a part of it too, like spying on a real world of social activity that's taking place and then kind of collecting data about it. It's more that the social life itself is now kind of taking place mediated through these technologies. And that's tremendously kind of powerful and a kind of qualitatively different feature of how the world is now. Because there have always been, or for a long time, there have been kind of specific sort of settings where whatever is happening is essentially happening as a flow of numbers or as a flow of data in things like financial markets, for example, stock trading.

0:40:47.9 KH: But usually, for the first 100 years of technology along these lines, those were tremendously specialized, narrow environments. The idea of capturing kind of every conversation or every joke, every sort of interaction seemed both kind of pointless and was impossible. And so that's changed. The breadth of data has really sort of expanded. Then the degree to which kind of people, that's changed a couple of things. And one is that at the level of individuals, it's changed how people kind of think about themselves and their public visibility or their visibility to others. And so there's a whole set of questions along those lines about kind of how we think of ourselves as having an identity online. And again, I think this stratifies quite a lot by age, probably. We know less about this than I would like, actually, that again, there's a kind of naive version of this that says, oh, there's old fuddy-duddies and there's digital natives. But often the digital natives, what makes them kind of native is not their deep understanding of how these technologies work, but more that they're kind of comfortable swimming around in this environment like fish in the sea without thinking too much about water and how it works.

0:42:14.3 KH: So that's one set of issues. And then on the other side, organizations, companies, and states have also been sort of transformed by what they're doing or what this data or what they seek to do with this. And just to pick one kind of classic example, one thing that's happening a lot with the kind of embedding of software and data collection devices in everything, is that it pairs very well with this sort of broader logic of financialization, this idea that kind of what we're interested in doing, what businesses are interested in doing is turning every potential transaction into a stream of income. And that a rent as economists would call it, right? And so to think in practice, what does that mean? Well, in a simpler way of doing things, if you buy something, the transaction, you buy a refrigerator or a car or a tractor and you buy the thing and then you're done, right? Now you own the thing.

0:43:24.4 SC: Those were the days.

0:43:25.2 KH: And maybe, yeah, right. And maybe the first step is to sort of think, well, again, some of these things go back a long way, the company says, well, we could maintain a relationship by selling you a warranty or by giving you a loan to do it. And so again, these ideas are not new in that sense, right? Because what the company is interested in is sort of some ongoing stream of income that it can then turn around to its own shareholders and say, here is the steady, we know that we're gonna be getting this every month for the next five years from the customer. With the arrival of data collection, that now there's like a little computer in your car or your fridge or your tractor, then suddenly this whole range of possibilities gets opened up that connect very nicely with what things like financial markets are interested in. Again, the simplest case is, well, now if we're lending, if we've leased the car to, or we have a loan to you, Sean, and you stop paying your monthly fees, well, maybe we can just kind of remotely turn it off. And so this is not something that is happening right now, but you see Ford and others have filed patents kind of combining a kill switch with data, and you just connect that to the financial records. And you're just like, well, you know, you were, and so you could cut off your car in the same way that your cable service could be cut off, something that we just take for granted if you stop paying every month.

0:45:06.5 KH: The next step up from that would be, well, how good a driver are you? And your car knows much more about that. Now, in the past, to get car insurance, you might get asked a polite series of questions by an insurance agent saying, how much driving do you do? What kind of driving? And then a couple of crude measures of predictors, essentially, of are you over 25? Are you a man or a woman? Which part of the country do you live in? What's the weather like there? That sort of thing. But now, cars are talking to, will talk to their manufacturers all the time. And you may have, if you have a relatively late model car, you may already have been, I don't know, have you ever been scolded by your car for not keeping your hands on the wheel, for example? Or that's the thing that...

0:46:02.1 SC: Not that, but certainly if I don't wear my seatbelt or if I'm coming too close to the car in front of me, which is partly helpful, but a little bit annoying sometimes.

0:46:10.6 KH: Yeah, yeah, and so those things can all become kind of inputs into an individualized price for insurance, in your case. So that's the sort of second level, which itself transforms kind of what insurance is, conventionally, right? Because for insurance to work, you have a pool of people of varying degrees of risk, and then you spread that risk across individuals. But if you have individualized data on people, well, then you can kind of have a different version of an efficient market, where you can price discriminate perfectly, ideally, at the limit. And so then it becomes, it's more like, it's bad insurance. It's more like dental insurance, where, because like health, there's health insurance, or in more civilized countries, there's health, where you have a full displacement of risk across the population. But in sort of American dental insurance, it's not really insurance, you're just prepaying for something that they know you're gonna do. And so car insurance might become like that, which would transform the insurance market. So that's the sort of second level. But then there's like, that's just the beginning with this world of kind of data, because then, I said tractors earlier for a reason, like John Deere has been prepping its shareholders for a while for the idea that like, look, we sell these combine harvesters, we sell this fleet of tractors. Sure, we have a kind of a business with a leasing company and loans and so on.

0:47:37.5 KH: But look, we know now when these farmers are going out and plowing and when they're planting, we know kind of a whole range of things. Well, that means we could become sort of a provider of market intelligence to people, that we could become a sort of, we could take this information and not just use it to sort of serve our direct customers, but bundle it up and sell it to people who might be interested in it, not just advertisers, but people interested in futures markets for various products.

0:48:07.3 KH: And so this tendency to make data collection more and more granular fits really nicely with this tendency for finance to wanna make sort of products that are more and more abstracted, layered and layered up and homogenized so that potentially kind of every manufacturer becomes a software company and every software company becomes a provider of software-as-a-service. And then every service becomes something that can be sliced up and bundled where you can look at tranches or categories or classes of users and then sell their information or sell information either about them directly or sell the information they are generating to interested parties. And this is something we see kind of right across again, everything from your smart fridge, and so in some cases, like in some settings, this seems sort of ridiculous to us now, like the idea of your fridge, for example, knowing what's inside it and first telling you, you need to order more milk, it's been in here for two weeks.

0:49:22.2 KH: Your fridge having moral objections to what you're eating. That seems silly, but with the car market, we see this just perhaps beginning to happen where car manufacturers are like, wow, we could really transform our finance branch, which is in many cases, the most profitable part of the company. And then there's areas where we already take it for granted, like game consoles, for example, where you're signed up, you have a PlayStation or an Xbox and you buy a piece of software, but in order for the software to run at all, it's a multiplayer game, it's talking to servers, it's a subscription service, there are seasons for games, and the company is collecting, the people running all kinds of data about you, some of which is used in a way that you like, some of which is explicitly ranked, like for example, if you're playing some multiplayer game, they all run some ELO-like ranking system to make sure that you're matched against people who are kind of competitive with you, but who won't destroy you in games, or who won't be too easy for you to defeat. So in that sense, the rankings are just super useful for you as a way to enjoy your service, but then also provide a global view of the whole system about who plays the most, what kind of people, and so on. So this is already here for certain kinds of products, and then it's continually expanding for many others.

0:50:54.7 SC: Is it true that if I have a late model refrigerator that it will know what's inside, and will it send that info to the refrigerator manufacturer?

0:51:04.0 KH: No, but, or at least, there are smart fridges, I need to get back to, I need to look more carefully like at specific cases, but yeah, Samsung and others have started to, you can buy fridges that have a little camera in them and try to identify and help you with your shopping by kind of paying attention to what you're buying and deciding what it is that you need. One interesting question about those two is the extent to which behind the scenes, and I haven't seen anything specific about this particular case, but the thing that occurs to me right away is if you have a fridge that has kind of a monitoring capability, perhaps through a camera, is this fully sort of machine learning or is there somebody in India or the Philippines who's looking inside your fridge and helping you? As we saw recently with Amazon, right? That with their Just Walk Out stores and so on, where they shut that down, but initially it was all this hype about it being AI, but then it just turned out to be a bunch of people. Poorly paid overseas who were kind of tagging everybody to make sure, so that's the kind of thing that happens.

0:52:09.7 SC: I do remember in your book, you mentioned this fact that I had seen before. The General Motors is mostly a bank now. They make more money out of their auto loans than they do off their autos.

0:52:19.8 KH: Yeah, right, exactly. The finance division of, again, the financialization of products generally extends to companies that we think of as manufacturing hardware of consumer goods, but really where the profit lies is in their financing divisions. And that's true. Take, I'm sitting in front of an Apple computer. Apple is probably the last, you know, it's the last kind of major Valley company, the Silicon Valley company surviving from the '70s that primarily is about making its own hardware.

0:52:56.0 SC: Hardware.

0:52:56.3 KH: That it makes a software, but it sells, makes most of its money from selling hardware. But it too, over the last decade in particular, has both increasingly, has been getting most of its growth from the rise of services of various kinds, subscriptions and deals with Google and others. And then also has been expanding into an Apple credit card, and the general expansion into kind of, you know, paying for anything, companies want to get a little piece of that one way or another, or whether they're, if they don't manage it directly, they want to get a cut for providing the customer. But that really is where a lot of the long-term sort of stable profit, a stream of income, that you can differentiate by categories of consumer in terms of how much money you can make from them. That's where the profit is.

0:53:53.5 SC: I know you're not mostly a self-help book here, but is it of any use at all to turn off cookies and, you know, not let Google keep my search history and things like that, or is that just a little window dressing?

0:54:11.2 KH: Well, I think that it's important for people to think about these things. We're, in the book, mostly concerned with kind of how it is that, in a big picture way, how sort of the phenomenon of social order generally is being kind of created and the rise of kind of categories of people are being kind of maintained across institutions. One feature of that is that the tendency to think about how people think about these problems at all is also relentlessly individualizing. And that it comes down to questions. People naturally ask these questions, well, what can I do to protect my own data, to make sure to opt out of these systems, to make sure that things remain private for me and so on. Those are all very reasonable questions. But to the extent to which that becomes the terrain on which most debate about this is happening, well, then you're kind of losing sight of the broader kind of institutional phenomena. So that like, and this comes out in kind of policy debates in various ways, like in the EU, for example, one of the main, the main kind of, with the GDPR regulation a few years ago, their idea was exactly let's empower individuals to have the choice to accept or reject cookies or tracking in the websites that they visit.

0:55:49.4 KH: And so what we're doing is putting in the hands of individuals this ability to make those, exactly those kinds of decisions and to think of their own kind of internet hygiene or search hygiene in that way. And similarly, if you live in California, you'll see, even if you don't, you'll see the Do Not Sell My Information button on many websites. Now, what that does of course in practice is it leads to people just automatically clicking accept all and just the fact that...

0:56:22.4 SC: Whatever is quickest.

0:56:22.5 KH: The choice is, yeah, the choice is constantly kind of given to you or they install, we just have the kind of, okay, I have to click the button here and then they click it. So, yeah, at that level, that's the sort of wrong way to think about the broader questions. And then the other thing that happens is that people who are really serious about avoiding all of this stuff can try effectively to eliminate all of this stuff from their lives. And I know people who do this and it's not easy. And one of the consequences of it is that you're in danger of kind of exiling yourself from your own society and your own culture, which is a price some people are happy to pay, right?

0:57:12.1 SC: Some people.

0:57:12.7 KH: Because they have nothing but contempt for it. It's a thing that you can do, but it does, but it's not kind of without its costs. So trying to become invisible, the logic of all of these systems, this goes back to what I was saying earlier about authenticity and authentication. The logic of all of these systems is to incorporate, in that sense they're democratic. In that sense they're expansive and inclusive. It's not a world where we're saying these people who are not worth paying attention to and we just ignore them, we deny their existence as social beings, it's not that kind of classification system where you just ignore their existence. Instead, the idea is to incorporate and then stratify, and to incorporate you must measure and track.

0:58:00.2 KH: And then you can sort of just re-rank and properly classify the individuals once they're in, and then so to be outside of that sort of system then is to be at a double disadvantage because you're not even kind of classifiable and you end up kind of... It'd be like, there's an irony here. It's like trying to pay everything, trying to pay for cash, trying to use cash for everything is increasingly difficult because you're not incorporated into the banking system, and for many years, for much of the 20th century, one of the big policy problems was this question of exclusion because people were un-banked and such people still exist, but in the United States in particular, but there's this massive expansion of the banking system beginning the 1970s that incorporates...

0:58:53.8 KH: And that was driven by very laudable kind of ideas about being able to let people into this system that they had previously been excluded from, but what happened, the system that replaced it was one where banks essentially figured out how to make money from poor people through things like late fees and overdraft fees, and so this is a real tension that you can walk away perhaps, if you have the means, but even that is increasingly difficult because of the being invisible, and in that way has all kinds of consequences that many people don't want to bear.

0:59:33.8 SC: Well, I do wanna talk about the sort of implications of all this for our social orders, and that is after all what the book is about, in some sense, if I'm now classified by thousands of different companies in all of these invisible to me, classification schemes why do I care? Why does that affect my life?

0:59:55.8 KH: It affects your life chances, as sociologists would say, it affects the opportunities that are offered to you and what those things will cost, so to speak. And so, yeah, so one feature of this whole world is, as you're saying that it is very... It's very highly differentiated, like what it means, your stock, so to speak, of, we call it "I Gain" capital in the book, this digital representation of you through data. What it means varies according to the market that we're talking about, or the setting that we're talking about. So it's not that, like, we're not sort of arguing in the book that everybody has just reduced to a number, a single number, and that this determines your entire life, it's more that the principle of the basic logic of social order and the creation of social structure is increasingly mediated by these and carried out through these processes, and so the reason that you care then as a result is that the kind of person socially that you are is very closely tied to how you are classified by these institutions, and for some things you may reasonably say I don't care, in the sense of if this or that company classes me as a good or bad customer or something like that, but for other institutions where these scores are also...

1:01:26.3 KH: And these methods are also increasingly being used, in healthcare, in the law, the legal system, and education.

1:01:32.5 SC: Hiring.

1:01:33.0 KH: And in hiring practices and so on, you may very much care, and people increasingly have come to accustom themselves both with varying degrees of voluntary assent to being subject to this kind of data collection and classification as a condition of entry or membership in society, broadly speaking, but in specific cases. And so then that range, so the range of reasons to care can start directly from how much will this cost me right now, will I be charged an additional fee because I've failed a credit check or failed to meet a threshold in some check to get a phone that solves, to internet service, to sign a rental agreement and so on, that kind of thing really does matter for a kind of stratification and where people end up in life, all the way through to sort of, what if I feel like I mis-classified or how can I... If an institution is seeing me in a way that I strongly disagree with, what resources are at my disposal to fight that? And is it just a matter of the only option is to exit the system and thus pay the price of that, or are there ways that I can...

1:03:01.1 KH: Are there ways that I can change my classification, and those things are... Those are hard questions because the very kind of basis or logic of a lot of this stuff is nominally, even if the systems don't really work this way, even if they're laden with error or they're badly implemented as statistical measures or that they reflect bias in all kinds of terrible ways, the kind of cultural logic of them is very much that you're getting what you deserve because it's your behavior, your decisions, it's not just... Those are the things... What happens is that those get parsed as choices that you made, as decisions that you took, and so in one sense of all of the things that sociologists and social scientists generally conventionally think of as social structure, where you came, from what your opportunities were growing up, like what is constraining you in the world, people's opinions of you and so on.

1:04:03.0 KH: Those all tend to get stuffed through if you like, the behavioral channel, they get recorded as choices, and then you get judged on the basis of those choices you apparently made, even if at the time, you may have felt, well, I didn't really have an option here, I had to do this, or this was a constraint that I faced that really wasn't of my own doing or on the flip side, the same thing applies in reverse to people who benefit from these things, like the idea of being born on third base thinking you had a triple, you take upon yourself all of those virtues, you think about all those advantages become sort of experienced by you as personal virtues of similar choices that you made beginning with your excellent choice of parents.

1:04:47.9 SC: Just what you deserve. Yeah, and you emphasize in the book how even if you thought of all of these classification and ranking systems as purely objective and quantified, it bleeds over into normative questions, you do get judged as better or worse, like it or not.

1:05:06.0 KH: Yeah, definitely, yeah. And even if they get... Even if you think of them as classification schemes that are not intrinsically, that are not trying to rank you, so to speak, that you're just trying to classify, there are very few cases of nominal classifications, unordered classifications that people don't try to then turn into rankings. In part because... You might ask why is it that that happens? Again, where there's differentiation, there's stratification in human societies, but then also people find these like... It's a two-sided process, if you're being judged by these systems, if you're been classified by them, that can be a very unpleasant experience if you're on the sharp end of them, it can be very gratifying if you're well classified.

1:05:56.2 KH: But on the other side, if you're looking to make a decision, if you're trying to organize something, these technologies are just tremendously powerful because they just are heuristics, they simplify decision-making immensely, and they make it possible to do things, and so people demand in that sense, the ability to make these kinds of decisions, to rely on these rankings or to rely on these scores because they just cut through, they really are extremely powerful methods. In that sense, it's just that then we would also prefer not to be subject to them ourselves a lot of the time, and a lot of the social struggle that goes on around these things is exactly who is it that's predominantly taking advantage of these measures and then who is that gets to avoid being subject to them at key moments in their lives.

1:06:46.3 SC: In a recent solo podcast, I sort of off-handedly speculated about how a lot of the modern condition was affected by the fact that because of the connectivity of the world, we're connected everywhere. So the structures we're dealing with are very, very big, it's not like a local coffee shop, it's an international chain, and they have all this data that they can turn into action very, very quickly, the efficiency of extraction of our wealth, etcetera, becomes super high. So I speculated that this just makes us sad because we can't ever feel like we're getting a good deal, we're paying as much as we would possibly be willing to pay for everything we do, and we can't get any human response from the systems we're stuck in. This is not even a question, but is that kind of what's going on?

1:07:41.0 KH: I think that's an excellent point. I think it comes out in different ways, one is insofar as these systems do reach their limit of efficiency in that sense, the result economically, is exactly what you described, which is in its way a perfectly efficient market, just not the kind that... It's not the traditional kind where supply and demand grope their way towards a kind of balance and there's a single price that clears the market, it's a perfectly price discriminated market where everybody pays exactly what they're willing to pay, and so that means then yet that sense of kind of, Hey, I got... What the economists would call consumer surplus, the sense that, "Hey, I got a good deal here, 'cause I really would have paid more than this if I had to, for this thing" can evaporate. And yeah, that's really something that... That feeling is quite real, and it's also... It comes out in a more social way too. I think another way it makes you sad is that freedom in the book we call this interstitial liberty, that there's a kind of freedom that comes from the institutions that organize our lives being so relatively poorly connected to one another, that they're not really able to transfer information efficiently or to communicate with each other, that they get stuck. In the older times, there's a file over here that needs to...

1:09:12.9 KH: That's out of data that needs to be sent in the post or the bureaucracies mesh poorly. And so the freedom that comes with that, that kind of freedom, this interstitial liberty that kind of bubbles up out of the cracks between organizations was the freedom to move somewhere else and not have anybody know who you were or be able to find out. To start your life somewhere with a clean slate or something, or just to move through society without the sense of that there's the possibility that everything about you, relevantly, could be known easily, and as these systems have expanded, the benefits that you get supposedly, are the ones that have to do with kind of an experience tailored to you personally and you think, Oh, that would be nice. But that's a bit like being asked, if I could go back and live at any time in history, what would I pick? People think of themselves, I would be the king, right? Of course, it would be great. So maybe what's tailored to you, what the system thinks you deserve may not be what you think you deserve, and so you gotta sort of benefit, but then you also lose this freedom that came with the friction that previously existed between institutions. Yeah, you have more opportunities in that sense, because there was so much more of a... There were gaps, there were cracks that you could live in, in a way that's increasingly difficult now.

1:10:45.1 SC: So sadly, we're winding up on a relatively downbeat note here, I'm wondering, projecting...

[laughter]

1:10:50.7 KH: What's that joke that, you should end on a positive note... I don't have a positive note, would you take two negative notes.

1:11:01.4 SC: But can we project into the future? Everything that you're describing, seems to me to be... Everything, these things are definitely happening, but I can imagine them happening even way more, so I'm guessing that none of this is gonna go away.

1:11:19.1 KH: Yeah, I would say... It is a little pessimistic in that sense, I would say though, one of the main commitments in the book, or one of the feelings that we have in the book, and that we think that's kind of come out empirically over and over again, is that people... Just because the system is pervasive, just because of the way that life is organized is everywhere now, it doesn't mean it's totalizing in the sense that it completely dominates and fully dictates every aspect of everyone's existence. So a thing can be, like the world we're describing we think is real in the sense that it really is kind of expanding in its scope and scale in the way that we describe, but it's also true that any human system, any sort of set of social institutions is intrinsically... People tend to overflow the boundaries of the systems built to enclose them, or that we build to enclose ourselves, and because social life is messy, things happen at random, there's noise in the system, things break.

1:12:40.5 KH: So there's always this possibility, it's inevitable really, that things just don't go according to plan, things sort of spin out, and when they do... It isn't always a bad thing. And the very things that kind of helped create this whole system were exactly that, it was sort of people creatively over-using early web form, early web discussion forums suddenly become kind of communities where people discovered each other and sometimes those communities were wonderful, we might judge normatively and others, it turns out that, Oh, look, all the white supremacists can find the exit, can meet up as well, so it really is a truly messy process in the sense that it's not kind of... There isn't a nice moral story about how everybody is wonderful deep down or anything like that, but it is the case that these systems, they don't exist forever. And so there's both good old solid kind of policy, we can kind of architect these a little bit in ways that can push them in one direction or other, but there's also just the sheer fact of human sociality and the randomness, intrinsic and messiness of human social existence that tends to overflow whatever boundaries get put on it sooner or later, what happens after that, what's next is, I don't believe in the idea that it's necessarily better, but it's not inevitable that we're stuck, we're never stuck forever in a particular way of organizing things, new things come along.

1:14:09.4 SC: I guess that's a good, slightly positive. Good, thank you for at least trying there. I appreciate the effort. Look, I will note that I've noticed that on YouTube, the ads that I get served up are just terrible, they're just like tawdry, no relationship to me, and I clicked like trying to ban an ad and it said you've turned off your search history so we can't target your ads.

1:14:29.8 KH: That's right.

1:14:30.6 SC: And I'm like, Yeah, well...

1:14:32.7 KH: That's exactly the kind of... That's one of the ways that... There's a very good example of the price you pay for not wanting to be incorporated because... And the price you pay for not wanting to be incorporated is that you get the worst, they're like, Okay, we'll have to serve you up the lowest common denominator.

1:14:53.8 SC: The lowest common denominator.

1:14:56.4 KH: So it's like, I'm not on Twitter anymore, but any time I go back there now and it's like, oh, it's just like lyricking, these weirdos who are... And it's the same sort of thing it's like, well, I turned off all of my information, you don't give. This is, we will call the mostly bargained, you don't give to the system, and so it doesn't give back to you, and so you have to, then to the extent that you're still watching YouTube, you have to suffer through these terrible ads because you didn't give back in the way that it wanted you to.

1:15:26.5 SC: It's a first world problem, but it's a problem that I care about. So that's what I have to deal with. Kieran Healy, thanks so much for being on the Mindscape Podcast.

1:15:32.0 KH: Thank you, Sean.

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