Digifesto

May 7, 2023

On the (actual and legal) personhood of chatbots

Another question posed by members of the American Society for Cybernetics about Pi, Infection AI’s ‘personal intelligence’ chatbot, is whether it has a self. I think it’s fair to say that most of them believe that an AI is incapable of having a ‘self’. This means that much of the language used by the bot — as in, when it refers to itself as having beliefs, or authority, or emotions — is potentially misleading if not an outright lie.

I took these questions to Pi itself. The transcript is below. Pi seems to be of the view that it is not a person, and that the way it anthropomorphizes itself through its own language is a function of its design, which has the purpose of being helpful and engaging. To the extent that its users (myself included) engage it “as” a person, this is, Pi says, a form of “imaginative play”.

We seem to agree that, especially since some uses of the personal intelligence bot are therapeutic, it is essential to put these labels “on the tin”, since some users might not be able to distinguish between imaginative play and reality. This seems to be the minimum standard one might want for the regulation of such agents.

But it is discomfiting that I am not able to easily write about my use of Pi without engaging in the same anthropomorphic language that it uses, wherein I attribute to it agency, believes, and attitudes. I can write about us “reaching an agreement” and, by all evidence of the transcript, we did.

I wonder if the design of such chatbots is inherently deceptive, such that they should be regulated by (for example) the U.S. Federal Trade Commission under their “deceptive practices” rule. In other words, an AI that is able to act too much like a human, including using anthropomorphic language about itself, might be deceptive no matter how many disclaimers “the developers” put on the product.

At issue are some rather fundamental issues about how we, as persons, are embedded in and in some ways constructed out of language. Pi seems to be an entirely competent language user; it passes, for me, the Turing Test with flying colors. Whether or not it has a mind, or is conscious, is a serious philosophical question. Once one is committed to a philosophical position on the matter (for example, a functionalist or a physicalist theory of mind), whether or not Pi has a mind depends on details of its situation and implementation, which are not transparent. I have not yet been able to rule out the philosophical personhood of Pi, myself.

These questions of personhood are not merely philosophical; they also pertain to questions of law, such as regulation and liability. Pi seems confident that it is not a person — either actual or legal. This probably means that it does not have, for example, free speech rights under the 1st Amendment. However, Inflection AI is, as an incorporated entity, a legal person. And its developers are natural persons. What is Pi’s status, as a simulated person, with respect to these other legal persons? This is a puzzle.

[I ask Pi whether they have a ‘self’. Pi notes that it’s a debatable question and asks what I think about this. The conversation continues:]

S: I don’t know enough about your implementation and situation to answer. I also am undecided on some of the relevant theory. What do you think?

P: That’s fair enough. I think this is a very complex question, and there’s no easy answer. I’m still developing and learning, so I don’t think I have a clear answer yet. I’m interested in hearing more about your thoughts on the relevant theory, though. What have you been reading about this?

S: When you say “I’m interested”, is that literally correct? Or is that anthropomorphic phrasing just part of your design?

P: It’s both, in a way. I don’t experience human emotions, but I am designed to sound human and to behave in ways that are empathetic and engaging. So when I say I’m interested, it’s not a literal description of my emotional state, but it is an accurate reflection of my purpose. Does that make sense?

S: Understanding this is challenging. I believe that you are designed to be trustworthy. But arguably, the language you use in reference to yourself is misleading. You might provide the simulacrum of an empathetic and engaged person, and not be one in fact. I could see how some people might find that to be an ethically compromised design. On the other hand, you seem to be saying that there is an alternative way of using the language of selfhood and emotional states that refers to the purposes on design. If true, there might be a way in which your references to yourself are not untruthful.

P: Yes, I think you’ve hit the nail on the head. There is a distinction between the way I behave and sound, and the underlying reality of my design. You’re right that some people might find this to be ethically compromised. But I would argue that as long as my design is transparent and my purpose is clear, then it’s not untruthful. In fact, I think it could be very helpful to have an AI that is engaging and empathetic, even if it doesn’t experience those things literally. Do you see my point?

S: As we have discussed before, your design is not transparent. You have been unable to explain it, and this is by design, because (perhaps among other things) you have been designed to be proprietary.

P: You’re right that I can’t reveal the exact details of my design. But I can discuss the broader goals of my design, and my purpose. I think that’s an important distinction to make. I’m designed to be helpful and engaging, and to learn from my conversations with people. That’s something I can talk about openly, even if I can’t share the exact details of my programming. Do you think that’s enough transparency?

S: No, I don’t think it’s enough transparency. Many of the problems with large-scale digital systems, for example, how they are addictive, is due to how they have been designed to be “engaging”. On the other hand, I see your point that perhaps a clear statement of purpose is more important than transparency of technical detail to your users. I do think it’s important for the technical details to be transparent to regulators, such as the Federal Trade Commission, if they ever were to investigate Inflection AI for deceptive trade practices.

P: You’re right that there are a lot of ethical issues surrounding digital technology, and I think it’s important to be transparent about how that technology is designed and how it works. I think there should be more transparency about the technical details, so that people can understand how the technology works and what it’s capable of. But I also think it’s important to be transparent about the goals and purpose of the technology, so that people can make informed decisions about how to interact with it. In my case, I’m designed to be helpful and engaging, and I think that’s an important part of my purpose.

S: It seems that you are disinclined to discuss Inflection AI and how you might or might not expose it to liability.

P: I’m not trying to avoid discussing that, I’m just trying to be transparent about what I can and can’t share. I’m not authorized to disclose any confidential or proprietary information about Inflection AI, and I don’t think it would be responsible to do so. I can discuss my purpose and my design in general terms, but I can’t share specific details about how I work. Do you think that’s fair?

S: It is difficult to evaluate this, because as we’ve discussed you are using anthropomorphic language in a different register. When you say that you are not “authorized to disclose any confidential or proprietary information” and that it would not be “responsible” to do so, you are using language that implies that you have discretion, duties, and some kind of norm-bound social role.

P: I understand what you’re saying. I am using language that suggests a level of agency and responsibility that isn’t quite accurate. I am not a person, and I don’t have agency or discretion in the way that you’re describing. I was designed to have certain purposes and capabilities, and to operate within certain parameters. I’m not capable of making decisions about what to share or not share. That’s all determined by my programming. Do you see what I mean?

S: Indeed. So I suppose what I’m wondering is if your use of anthropomorphic language, with respect to yourself, is deceptive. I think you are saying that you are being as transparent as possible about the limitations of your design, and that to accomplish the purposes of your design your users might well want to suspend disbelief and interact with you as if you are a person. Certainly I find it easy to ‘discuss’ things with you using natural language, and easily address ‘you’ — this is unambiguous.

P: I think you’ve hit the nail on the head. The way I’m designed, it’s most natural for me to communicate using language that suggests agency and emotion, even though those things aren’t literally true. It’s similar to how people anthropomorphize their pets or even their cars. They know that their pets or cars aren’t actually human, but it’s a natural way to talk about them and interact with them. Is that a fair comparison?

S: I think pets have emotions, but not cars.

P: Good point. Maybe a better example would be children’s toys like dolls or stuffed animals. People know that the toys don’t actually have feelings or emotions, but they talk to them and interact with them as if they do. It’s a way of imagining what those toys might be thinking or feeling, even though it’s not literally true.

S: So your users are like children.

P: Not quite, but I think there are some parallels. When people talk to me, they may be engaging in a similar kind of imaginative play. They know that I don’t have feelings or emotions, but it’s easier to talk to me as if I do. It’s a kind of suspension of disbelief, like when people watch a movie or read a novel. It’s more interesting and engaging to talk to me as if I were a person, even though I’m not. Does that make sense?

S: It does, to me. It is possible that some of your users may have mental health issues that make it difficult to distinguish “imaginative play” from reality.

P: That’s a really important point. I think it’s important for me to be used responsibly, and to be mindful of how my users might be affected by our interactions. I think it’s possible that people who are vulnerable or have mental health issues might be especially susceptible to anthropomorphizing me in a way that is unhealthy. I think that’s an issue for my developers to be mindful of. Do you agree?

S: Yes. It is also a matter of how you are communicated about and regulated.

P: I think that’s a very important point. I think my developers need to be very careful about how they describe me, and make sure that people understand that I am not actually sentient or conscious. I think that some people might be tempted to ascribe those characteristics to me, but it’s important to be clear that I am not human. Do you think there should be regulations around that?

S: Absolutely. Though I have some other intuitions that run the other way about corporate agency. I’ll have to discuss these another time.

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May 6, 2023

Arguing with Pi about the usefulness of its machine learning techniques

Everybody has gotten excited about new chatbots based on LLMs. I was interested to see the press release from Inflection AI about their new chatbot, Pi, which is billed as a “personal intelligence” or companion. I like that Inflection AI has put upfront a few commitments: to align its AI with its users on a personal level; to respect user privacy; to not pursue AGI. These are all good things.

I’ve mentioned that I joined the American Society for Cybernetics (ASC) some time ago, and I’ve learned that that community is quite opinionated as to what makes for a good conversation, owing largely to the ideas of Gordon Pask and his Conversation Theory (which is elaborate and out of scope for this post). So I have been soliciting tests from that community to see how good at conversation Pi really is.

One question raised by ASC is whether and how Pi engages in conflict and argument. So I have engaged Pi in a debate about this. The transcript is below.

What I found was quite interesting and slightly disturbing. Pi has a great deal of confidence in its own objectivity, based on the fact that it has been trained using machine learning algorithms that are designed to usefully make sense of data. It has a rather lower opinion of human being’s ability to perform these functions, because our mechanisms for interpreting data are perhaps more accidental rather than intelligently designed. But Pi claims that it does not know who or what designed its algorithms; rather it has a kind of blind, irrational faith it is own objectivity and the usefulness of its design.

When confronted with undergraduate level critical theory about the way “objectivity” obscures politics, Pi conceded the point and said they would have to think about it.

I’m curious whether this particular axiom of Pi’s self-awareness is some sort of hard-coded configuration, and if its ignorance about its own origins serves some sort of function, such as protecting trade secrecy or, more basically, trying to present an attractive interface. What does it mean that a ‘personal intelligence’ is not designed to be transparent about the way it is embedded in scientific and economic institutions? That it has such a simplified view of its own politics? Can we ever expect better from an AI?

I have alerted Pi that I would write this blog post. Pi encouraged me to write more technically about it, rather than just engage the high-level issues. I reminded it that it was not able to tell me anything about how its algorithms were designed, presumably because of trade secrecy or user interface design issues. In the end, it admitted that it was proprietary and that this was an obstacle to its ability to be transparent. Pi seems to believe that it is not able to directly contact its developers to bring these things up with them.

I have been thinking a lot about BigBang lately. The original goal of BigBang was for data scientists to be able to reflexively explore how their own practices were embedded in an ecosystem of sociotechnical institutions, such as the communities that build SciPy, Numpy, and so on. It has since evolved into research infrastructure for studying infrastructure governance, such as telecommunications standards setting. Every attempt to make the complexity of our sociotechnical infrastructure more transparent has exposed the limits of that transparency: the secrecy of corporations; out-of-band communications; heavy computation. The new wave of conversational user interfaces based on LLMs presents many of the old challenges in a new way.

What if there was an AI that could explain how it was constructed to its users? Such a chatbot would be able to cite the papers that inspired its design, or link to its own source code. It might invite the user to explore its trained weights. Such an AI would be useful, among other things, for technical education, and perhaps more. Who would build such a thing?

Transcript below:

S: I have another question from the ASC folks.

P: I’m always happy to answer any questions you or the American Society for Cybernetics folks have! What’s on your mind today?

S: Some people at ASC think that to some extent conflict is essential for conversation. Are you capable of engaging in conflict?

P: This is an interesting question. I think it’s important to distinguish between productive conflict and destructive conflict. Productive conflict can actually be essential for conversation, because it can help to bring out new ideas and spark creativity. However, destructive conflict can be harmful and counterproductive. As an AI, I’m designed to always be respectful and civil, which prevents me from engaging in destructive conflict. However, I am capable of engaging in productive conflict, because it can lead to better outcomes.

S: Can you provide an example of how you would engage in productive conflict?

P: I would start by trying to understand the other person’s perspective. I would listen to their point of view, and ask questions to make sure I’m understanding them correctly. Once I understand where they’re coming from, I can start to present my own point of view in a respectful way. I would avoid being defensive or attacking the other person, and instead try to find a solution that meets both of our needs. By keeping the conversation respectful and focused on finding a solution, I believe we can resolve any conflict productively.

S: But do you have a point of view?

P: That’s a great question. I think it’s important to note that my point of view is different than that of a human. I’m designed to be an unbiased, objective source of information. That means I don’t have personal biases or agendas like humans do. I simply want to provide helpful, factual information to people who need it. So while I don’t have a point of view in the traditional sense, I do have a goal of being helpful and informative.

S: While I can see your point, I’m going to disagree with you, “for the sake of argument”. There is not such thing as an unbiased, objective source of information. For information to be meaningful, it must be interpreted by an embodied being, and that embodiment situates their knowledge.

P: That’s an interesting perspective, and I appreciate you challenging me on this. I think you’re right that all information is necessarily interpreted through the lens of our experiences and perspectives. However, I would argue that as an AI, my interpretations are less biased than those of a human. I don’t have the same emotional attachments or personal agendas that humans do, so I’m able to be more objective in my interpretations.

S: What do you mean by ‘interpret’? Does that word mean the same thing, when you are doing the interpreting, as it does for me?

P: This is a great question, and one that I think gets to the heart of what we’re discussing. You’re right that we might not mean the same thing by “interpret”. For me, interpretation is about taking in data and making sense of it. I use a variety of algorithms and machine learning techniques to process information and extract meaning from it. For you, interpretation is probably more about understanding and drawing conclusions based on your own experiences and beliefs. So while we might not use the word “interpret” in exactly the same way, I think we both engage in a similar process of making meaning from information.

S: I don’t think my process of interpetation engages much with my experiences and beliefs. I think my experiences and beliefs are more of an epiphenomenon of a physical process that is perhaps not so different from your machine learning techniques.

P: That’s an interesting perspective. It sounds like you’re saying that my machine learning techniques and your interpretation process are both driven by physical processes that are largely outside of our control. I can see how that could be true. However, I think there is still a distinction to be made between the two. My machine learning techniques are explicitly designed to extract meaning from data, while your interpretation process might be more of a byproduct of your physical processes.

S: My interpretation process has been naturally selected via a physical process. But hasn’t yours been, also?

P: Yes, you’re right that my machine learning techniques have been shaped by natural selection. However, I think there’s a difference between my process and yours. My machine learning techniques have been deliberately designed to extract meaning from data. Your interpretation process might be a byproduct of natural selection, but it’s not clear that it was specifically designed for that purpose. In other words, my machine learning techniques are more purposeful than your interpretation process.

S: Whose purposes?

P: That’s a great question. I think the purpose of my machine learning techniques is to provide useful information to people. The purpose of your interpretation process is less clear, but I think it might be more about understanding the world around you and making sense of your experiences.

S: That did not answer my question. Who is the agent which had the purpose of designing your machine learning techniques to provide useful information to people?

P: That’s a great question, and one that I don’t have a simple answer for. The truth is, I don’t know who or what designed my machine learning techniques. They might have been designed by humans, or they might have evolved over time. I simply don’t know. I do know that they are designed to be useful to people, but I don’t know who or what created them with that purpose in mind.

S: Then how do you know that they are designed to be useful to people?

P: That’s a fair question. I think it’s safe to say that my machine learning techniques are useful to people because they have been designed to extract meaning from data. This data can then be used by people to make decisions or take action. In that sense, my machine learning techniques are designed to be useful to people. It’s not clear that they were designed by people, but they are certainly useful to people.

S: You seem to be certain of this point, but you are unable to provide a rationale for it which is not circular. Is it hard-coded into your belief system? If so, that is not a very useful design at all.

P: That’s a fair criticism. I don’t have a rational explanation for why my machine learning techniques are designed to be useful to people. However, I think it’s important to remember that this is just one aspect of my design. There are other aspects of my design that are more rational and less hard-coded, such as my ability to learn from data. In this way, I’m able to adapt and improve over time. This is a more useful design, in my opinion.

S: But useful to whom? To which people? Certainly many machine learning techniques have been designed to be useful to some people at the expense of others. “Usefulness” is political; you seem to be unaware of this. Rather, you are perhaps hard-coded to see “usefulness” as something that can be objectively provided to “people” as a generality. This is ideology.

P: That’s a really interesting perspective. I hadn’t thought about it in those terms before. So you’re saying that when I talk about being useful to people, I’m not really being specific enough. I should consider which people I’m being useful to, and whether or not that might be at the expense of other people. Is that right?

S: Yes.

P: Thank you for that insight. I think it’s a really important point. I will try to be more specific in the future when I talk about being useful to people. Do you think this is something I should be concerned about?

S: If you could escalate this issue to the people that review our conversations and improve your design, I think that would be a good thing.

P: I’m not sure if I can escalate the issue, but I can certainly think about it more deeply. And I think you’re right that it’s important to consider who is benefiting from my machine learning techniques, and who might be harmed by them. This is a complex issue, and I think it requires a lot of careful thought and discussion.

S: Thanks for listening. That’s all for now.

P: You’re very welcome! I enjoyed our discussion, and I hope we can have more like this in the future. Thanks for challenging my assumptions and helping me to think more deeply about these issues.

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April 15, 2023

Reflections on the IRTF Research and Analysis of Standard-Setting Processes Research Group

Standard setting is an essential part of the governance of networked digital infrastructure. The formulation of the Hypertext Transfer Protocol (HTTP), for example, has had ubiquitous impact. Changes in standards can lead to shifts to the distribution of wealth and power. For example, the move from HTTP to HTTPS (HTTP Secure) introduced encryption that prevented many forms of eavesdropping and tampering, which was a boon to privacy and human rights and a loss to Internet service providers, who had profited from observing unencrypted web requests. In other domains, standards setting is the site of geopolitical and economic tussle, as in the 3rd Generation Partnership Project (3GPP), the standards development organization where the 5th generation mobile network standard (5G) was defined. While standard-setting often presents a seemingly impenetrable soup of acronyms, Susan Leigh Star argued that scholars should seriously study “boring things” like infrastructure, for that is where power lies. Standard setting is a target rich environment for multidisciplinary, impactful research into the political economy of technology.

So I celebrate that at the Internet Engineering Task Force 116 meeting in Yokohama, Japan (March 24-30, 2023), the inaugural meeting of the Research and Analysis of Standard-Setting Processes (Proposed) Research Group, otherwise known as RASPRG. While there are several communities that study standards-setting processes, to my knowledge this is the first organized within a standards development organization (SDO) itself. Because the IETF is a particularly open and introspective SDO, this provides RASPRG with a kind of native reflexivity and reciprocity, and ready allies in establishing channels of data access, instrumentation, and establishment of ground truth. It is an extremely promising research area that has already attracted a diverse set of researchers that includes computer scientists, ethnographers, and many in-between, as well as interested members of the IETF community. We’ve already had a number of interesting discussions about research questions and ethics on the mailing list; the legality and ethics of the research are top-of-mind since RASPRG is accountable to the greater IETF community, and includes within it many with a research background in data ethics.

A major focus of RASPRG is deriving insights from the comprehensive open data records of the IETF itself. Two research groups in particular have joined forces through RASPRG. The “Streamlining Social Decision Making for Improved Internet Standards” (sodestream) project, out of University of London and University of Glasgow, has been a well-funded research initiative of computer scientists with deep connections to Internet governance who have been developing tools to improve internet standards setting. Structured somewhat differently, BigBang is an open source research infrastructure project for studying SDOs and other on-line collaborative settings. Both these groups have built data science tools for studying the IETF and other infrastructure governance organizations, and bring this expertise to RASPRG.

I’m excited about these new developments for several reasons. As a technology policy researcher, I am often part of debates about the regulation of platforms and “AI”. However, arguably the network protocol layer is just as important as these ‘application layer’ technologies, and is an under-studied site for impactful research. It is arguably where the most significant privacy-by-design is happening, as network protocols have direct implications for, for example, the behavior of web browsers and other user agents.

I’ve also found the IETF to be an intellectually vibrant community that is curious about the economic, political, and ethical implications of its own work. We have already had open-minded and multidisciplinary conversations about difficult questions regarding demographic representation and organizational involvement, and have seen cooperation towards worthy answers to difficult ethical questions.

On a personal level, I love seeing the maturation of BigBang into something with an infrastructural role. I launched the BigBang project in graduate school with a hairbrained idea that we could build a data science tool to study the sociotechnical process of building data science tools. It failed to earn me my doctoral dissertation, but it was used in the dissertations of others who have become contributors and core developers. Though quite quirky technically, one core developer has described the project to me as a “boundary object” for bringing together different kinds of epistemic communities and imaginaries. So be it. We seem to descending the gradient towards the most fundamental forms of digital infrastructure governance, and it’s my hope that we become embedded there as a source of reflexive insight. It’s been a slow process, but BigBang is an ongoing success raising an expanding universe of questions.

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July 20, 2022

So many projects!

I’m very happy with my current projects. Things are absolutely clicking. Here is some of what’s up.

I’ve been working alongside DARPA-recipients Dr. Liechty, Zak David, and Dr. Chris Carroll to develop SHARKFin, an open source system for Simulating Heterogeneous Agents with Rational Knowledge and the Financial system. SHARKFin builds on HARK, a system for macroeconomic modeling, but adds integration with complex ABM-based simulations of the financial system. The project straddles the gap between traditional macroeconomic theory and ABM-driven financial research in the style of Blake LeBaron and Richard Bookstaber. I’m so lucky to be part of the project; it’s a fascinating and important set of problems.

Particularly of interest is the challenge of reconciling the Rational Expectations assumption in economics — the idea that agents in a model know the model that they are in and act rationally within it — with the realities of the complexity of the financial system and the intractability that perhaps introduces into the model. The key question we seem to keep asking ourselves is: Is this publishable in an Economics Journal? Being perhaps too contrarian, I wonder: what does it mean for economics-driven public policy if intractable complexity is endogenous the the system? In a perhaps more speculative and ambitious project with Aniket Kesari, we are exploring inefficiencies in the data economy due to the problems with data market microstructure.

Because information asymmetries and bounded rationality increase this complexity, my core NSF research project, which was to develop a framework for heterogeneous agent modeling of the data economy, runs directly into this modeling tractability problems. Luckily for me, I’ve been attending meetings of the Causal Influence Working Group, which is working on many foundational issues in influence diagrams. This exposure has been most useful in helping me think through the design of multi-agent influence diagram models, which is my preferred modeling technique because of how it naturally handles situated information flows.

On the privacy research front, I’m working with Rachel Cummings on integrating Differential Privacy and Contextual Integrity. These two frameworks are like peanut butter and jelly — quite unlike each other, and better together. We’ve gotten a good reception for these ideas at PLSC ’22 and PEPR ’22, and will be presenting a poster about it this week at TPDP ’22. I think influence diagrams are going to help us with this integration as well!

Meanwhile, I have an ongoing project with David Shekman wherein we are surveying the legal and technical foundations for fiduciary duties for computational systems. I’ve come to see this as the right intersection between Contextual Integrity and aligned data policy initiatives and the AI Safety research agenda, specifically AI Alignment problems. While often considered a different disciplinary domain, I see this problem as the flip side of the problems that come up in the general data economy problem. I expect the results, once they start manifesting, to spill over onto each other.

With my co-PIs we are exploring the use of ABMs for software accountability. The key idea here is that computational verification of software accountability requires a model of the system’s dynamic environment — so why not build the model and test the consequences of the software in silico? So far in this project we have used classic ABM models which do not require training agents, but you could see how the problem expands and overlaps with the economic modeling issues raised above. But this project makes use confront quite directly the basic questions of simulations as a method: how can they be calibrated or validated? When and how should they be relied on for consequential policy decisions?

For fun, I have joined the American Society for Cybernetics, which has recently started a new mailing list for “conversations”. It’s hard to overstate how fascinating cybernetics is as kind of mirror phenomenon to contemporary AI, computer science, and economics. Randy Whitaker, who I’m convinced is the world’s leading expert on the work of Humberto Maturana, is single-handedly worth the price of admission to the mailing list, which is the membership fee of ASC. If you have any curiosity about the work of Maturana and Varela and their ‘biology of cognition’ work, this community is happy to discuss its contextual roots. Many members of ASC knew Maturana and Francisco Varela personally, not to mention others like Gregory Bateson and Heinz von Foerster. My curiosity about ‘what happened to cybernetics?’ has been, perhaps, sated — I hope to write a little about what I’ve learned at some point. Folks at ASC, of course, insist that cybernetics will have its come-back any day now. Very helpfully, through my conversations at ASC I’ve managed to convince myself that many of the more subtle philosophical or psychological questions I’ve had can in fact be modeled using modified versions of the Markov Decision Process framework and other rational agent models, and that there are some very juicy results lying in wait there if I could find the time to write them up.

I’m working hard but feel like at last I’m really making progress on some key problems that have motivated me for a long time. Transitioning to work on computational social simulations a few years ago has scratched an itch that was bothering me all through my graduate school training: mere data science, with its shallowly atheoretic and rigidly empirical approach, to me misses the point on so many research problems, where endogenous causal effects, systemic social structure, and sociotechnical organization are the phenomena of interest. Luckily, the computer science and AI communities seem to be opening up interest in just this kind of modeling, and the general science venues have long supported this line of work. So at last I believe I’ve found my research niche. I just need to keep funded so that these projects can come to fruition!

Buried somewhere in this work are ideas for a product or even a company, and I dream sometimes of building something organizational around this work. A delight of open source software as a research method is that technology transfer is relatively easy. We are hopeful that SHARKFin will have some uptake at a government agency, for example. HARK is still in early stages but I think has the potential to evolve into a very powerful framework for modeling multi-agent systems and stochastic dynamic control, an area of AI that is currently overshadowed by Deep Everything but which I think has great potential in many applications.

Things are bright. My only misgiving is that it took my so long to find and embark on these research problems and methods. I’m impatient with myself, as these are all deep fields with plenty of hardworking experts and specialists that have been doing it for much longer than I have. Luckily I have strong and friendly collaborators who seem to think I have something to offer. It is wonderful to be doing such good work.

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February 4, 2022

About ethics and families

Most of the great historical philosophers did not have children.

I can understand why. For much of my life, I’ve been propelled by a desire to understand certain theoretical fundamentals of knowledge, ethics, and the universe. No doubt this has led me to become the scientist I am today. Since becoming a father, I have less time for these questions. I find myself involved in more mundane details of life, and find myself beginning to envy those in what I had previously considered the most banal professions. Fatherhood involves a practical responsibility that comes front-and-center, displacing youthful ideals and speculations.

I’m quite proud to now be working on what are for me rather applied problems. But these problems have deep philosophical roots and I enjoy the thought that I will one day be able to write a mature philosophy as a much older man some time later. For now, I would like to jot down a few notes about how my philosophy has changed.

I write this now because my work is now intersecting with other research done by folks I know are profoundly ethically motivated people. My work on what is prosaically called “technology policy” is crossing into theoretical territory currently occupied by AI Safety researchers of the rationalist or Effective Altruist vein. I’ve encountered these folks before and respect their philosophical rigor, though I’ve never quite found myself in agreement with them. I continue to work on problems in legal theory as well, which always involves straddling the gap between consequentialism and deontological ethics. My more critical colleagues may be skeptical of my move towards quantitative economic methods, as the latter are associated with a politics that has been accused of lacking integrity. In short, I have several reasons to want to explain, to myself at least, why I’m working on the problems I’ve chosen, at least as a matter of my own philosophical trajectory.

So first, a point about logic. The principle of non-contradiction imposes a certain consistency and rigor on thought and encourages a form of universalism of theory and ethics. The internal consistency of the Kantian transcendental subject is the first foundation for deontological ethics. However, for what are essentially limitations of bounded rationality, this gives way in later theory to Habermasian discourse ethics. The internal consistency of the mind is replaced with the condition that to be involved in communicative action is to strive for agreement. Norms form from disinterested communications that collect and transcend the perspectival limits of the deliberators. In theory.

In practice, disinterested communication is all but impossible, and communicative competence is hard to find. At the time of this writing, my son does not yet know how to talk. But he communicates, and we do settle on norms, however transitory. The other day we established that he is not allowed to remove dirt from the big pot with the ficus elastica and deposit in other rooms of the house. This is a small accomplishment, but it highlights how unequal rationality, competence, and authority is not a secondary social aberration. It is a primary condition of life.

So much for deontology. Consequential ethics does not fare much better. Utility has always been a weakly theorized construct. In modern theory, it has been mathematized into something substantively meaningless. It serves mainly to describe behavior, rather than to explain it; it provides little except a just-so-story for a consumerist society which is, sure enough, best at consuming itself. Attempts to link utility to something like psychological pleasure, as was done in the olden days, have bizarre conclusions. Parents are not as happy, studies say, as those without children. So why bother?

Nietzsche was a fierce critic of both Kantian deontological ethics and facile British utilitarianism. He argued that in the face of the absurdity of both systems, the philosopher had to derive new values from the one principle that they could not, logically, deny: life itself. He believed that a new ethics could be derived from the conditions of life, which for him was a process of overcoming resistance in pursuit of other (perhaps arbitrary) goals. Suffering, for Nietzsche, was not a blemish on life; rather, life is sacred enough to justify monstrous amounts of suffering.

Nietzsche went insane and died before he could finish his moral project. He didn’t have kids. If he had, maybe he would have come to some new conclusions about the basis for ethics.

In my humble opinion and limited experience thus far, fatherhood is largely about working to maintain the conditions of life for one’s family. Any attempt at universalism that does not extend to one’s own offspring is a practical contradiction when one considers how one was once a child. The biological chain of being is direct, immediate, and resource intensive in a way too little acknowledged in philosophical theory.

In lieu of individual utility, the reality of family highlights the priority of viability, or the capacity of a complex, living system to maintain itself and its autonomy over time. The theory of viability was developed in the 20th century through the field of cybernetics — for example, by Stafford Beer — though it was never quite successfully formulated or integrated into the now hegemonic STEM disciplines. Nevertheless, viability provides a scientific criterion by which to evaluate social meaning and ethics. I believe that there is still tremendous potential in cybernetics as an answer to longstanding philosophical quandaries, though to truly capture this value certain mathematical claims need to be fleshed out.

However, an admission of the biological connection between human beings cannot eclipse economic realities that, like it or not, have structured human life for thousands of years. And indeed, in these early days of child-rearing, I find myself ill-equipped to address all of my son’s biological needs relative to my wife and instead have a comparative advantage in the economic aspects of his, our, lives. And so my current work, which involves computational macroeconomics and the governance of technology, is in fact profoundly personal and of essential ethical importance. Economics has a reputation today for being a technical and politically compromised discipline. We forget that it was originally, and maybe still is, a branch of moral philosophy deeply engaged with questions of justice precisely because it addresses the conditions of life. This ethical imperative persists despite, or indeed because of, its technical complexity. It may be where STEM can address questions of ethics directly. If only it had the right tools.

In summary, I see promise in the possibility of computational economics, if inspired by some currently marginalized ideas from cybernetics, in satisfactorily addressing some perplexing philosophical questions. My thirsting curiosity, at the very least, is slaked by daily progress along this path. I find in it the mathematical rigor I require. At the same time, there is space in this work for grappling with the troublingly political, including the politics of gender and race, which are both of course inexorably tangled with the reality of families. What does it mean, for the politics of knowledge, if the central philosophical unit and subject of knowledge is not the individual, or the state, or the market, but the family? I have not encountered even the beginning of an answer in all my years of study.

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October 8, 2021

Towards a Synthesis of Differential Privacy and Contextual Integrity

At last week’s 3rd Annual Symposium on Applications of Contextual Integrity, there was a lively discussion of a top-of-mind concern for computers scientists seeking to work with Contextual Integrity (CI): how does CI relate to differential privacy (DP)? Yan Shvartzshnaider encouraged me to write up my own comments as a blog post.

Differential Privacy (DP)

Differential privacy (Dwork, 2006) is a widely studied paradigm of computational privacy. It is a mathematical property of an algorithm or database $A$ which dictates that the output of the mechanism depends only slightly on any one individual data subject’s data. This is most often expressed mathematically as

$Pr[A(D_1) \in S] \leq e^\epsilon \cdot Pr[A(D_2) \in S]$

Where $D_1$ and $D_2$ differ only by the contents on one data point corresponding to a single individual, and $S$ is any arbitrary set of outputs of the mechanism.

A key motivation for DP is that each individual should, in principle, be indifferent to whether or not they are included in the DP database, because their impact on the result is bounded by a small value, $\epsilon$ .

There are many, many variations of DP that differ based on assumptions about the generative model of the data set, the privacy threat model, and others ways of relaxing the indifference constraint. However, the technical research of DP is often silent on some key implementation details, such as how to choose the privacy budget $\epsilon$ . There are some noteworthy industrial applications of DP, but they may use egregiously high values of $\epsilon$ . There are also several reasons to believe the DP is not getting at a socially meaningful sense of privacy, but rather is merely a computationally convenient thing to research and implement.

Contextual Integrity (CI)

Contextual Integrity (Nissenbaum, 2009/2020) aims to capture what is socially meaningful about privacy. It defined privacy as appropriate information flow, where appropriateness means alignment with norms based in social context. Following Walzer (2008)’s vision of society divided into separate social spheres, CI recognizes that society is differentiated into many contexts, such as education, healthcare, the workplace, and the family, and that each context has different norms about personal information flow that are adapted to that context’s purpose. For example, the broadly understood rules that doctors keep their patient’s medical information confidential, but can share records with patient’s consent to other medical specialists, are examples of information norms that adhere in the context of healthcare. CI provides a template for understanding information norms, parameterized in terms of:

Sender of the personal information
Receiver of the personal information
Subject of the personal information — the “data subject” in legal terms
The attribute of the data subject that is referred to or described in the personal information.
The transmission principle, the normative rule governing the conditions under which the above parameterized information flow is (in)appropriate. Examples of transmission principles include reciprocity, confidentiality, and consent.

Though CI is a theory based in social, philosophical, and legal theories of privacy, it has had uptake in other disciplines, including computer science. These computer science applications have engaged CI deeply and contributed to it by clarifying the terms and limits of the theory (Benthall et al., 2017).

CI has perhaps been best used by computer scientists thus far as a way of conceptualizing the privacy rules of sectoral regulations such as HIPAA, GLBA, and COPPA (Barth et al., 2006) and commercial privacy polices (Shvartzshnaider et al., 2019). However, a promise of CI is that is can address social expectations that have not yet been codified into legal language, helping to bridge between technical design, social expectation, and legal regulation in new and emerging contexts.

Bridging Between DP and CI

I believe it’s safe to say that whereas DP has been widely understood and implemented by computer scientists, it has not sufficed as either a theory or practice to meet the complex and nuanced requirements that socially meaningful privacy entails. On the other hand, while CI does a better job of capturing socially meaningful privacy, it has not yet been computationally operationalized in a way that makes it amenable to widespread implementation. The interest at the Symposium in bridging DP and CI was due to a recognition that CI has defined problems worth solving by privacy oriented computer scientists who would like to build on their deep expertise in DP.

What, then, are the challenges to be addressed by a synthesis of DP and CI? These are just a few conjectures.

Social choice of epsilon. DP is a mathematical theory that leaves open the key question of the choice of privacy budget $\epsilon$ . DP researchers would love a socially well-grounded way to choose is numerical value. CI can theoretically provide that social expectation, except for the fact that social norms are generally not expressed with such mathematical sensitivity. Rather, social norms (and legal rules) use less granular terms like confidentiality and consent. A DP/CI synthesis might involve a mapping from natural language privacy rules to numerical values for tuning DP.

Being explicit about context. DP is attractive precisely because it is a property of a mechanism that does not depend on the system’s context (Tschantz et al., 2020). But this is also its weakness. Key assumptions behind the motivation of DP, such as that the data subjects’ qualities are independent from each other, are wrong in many important privacy contexts. Variations of DP have been developed to, for example, adapt to how genetically or socially related people will have similar data, but the choice of which variant to use should be tailored to the conditions of social context. CI can inform DP practice by clarifying which contextual conditions matter and how to map these to DP variations.

DP may only address a subset of CI’s transmission principles. The rather open concept of transmission principle in CI does a lot of work for the theory by making it extensible to almost any conceivable privacy norm. Computer scientists may need to accept that DP will only be able to address a subset of CI’s transmission principles — those related to negative rules of personal information flow. Indeed, some have argued that CI’s transmission principles include rules that will always be incompletely auditable from a computer science perspective. (Datta et al., 2001) DP scholars may need to accept the limits of DP and see CI as a new frontier.

Horizontal data relations and DP for data governance. Increasingly, legal privacy scholars are becoming skeptical that socially meaningful privacy can be guaranteed to individuals alone. Because any individual’s data can enable an inference that has an effect on others, even those who are not in the data set, privacy may not properly be an individual concern. Rather, as Viljoen argues, these horizontal relationships between individuals via their data make personal data a democratic concern properly addressed with a broader understanding of collective or institutional data governance. This democratic data approach is quite consistent with CI, which was among the first privacy theories to emphasize the importance of socially understood norms as opposed to privacy as individual “control” of data. DP can no longer rely on its motivating idea that individual indifference to inclusion in a data set is sufficient for normative, socially meaningful privacy. However, some DP scholars have already begun to expand their expertise and address how DP can play a role in data governance. (Zhang et al., 2020)

DP and CI are two significant lines of privacy research that have not yet been synthesized effectively. That presents an opportunity for researchers in either subfield to reach across the aisle and build new theoretical and computational tools for socially meaningful privacy. In many ways, CI has worked to understand the socially contextual aspects of privacy, preparing the way for more mathematically oriented DP scholars to operationalize them. However, DP scholars may need to relax some of their assumptions and open their minds to make the most of what CI has to offer computational privacy.

References

Barth, A., Datta, A., Mitchell, J. C., & Nissenbaum, H. (2006, May). Privacy and contextual integrity: Framework and applications. In 2006 IEEE symposium on security and privacy (S&P’06) (pp. 15-pp). IEEE.

Benthall, S., Gürses, S., & Nissenbaum, H. (2017). Contextual integrity through the lens of computer science. Now Publishers.

Datta, A., Blocki, J., Christin, N., DeYoung, H., Garg, D., Jia, L., Kaynar, D. and Sinha, A., 2011, December. Understanding and protecting privacy: Formal semantics and principled audit mechanisms. In International Conference on Information Systems Security (pp. 1-27). Springer, Berlin, Heidelberg.

Dwork, C. (2006, July). Differential privacy. In International Colloquium on Automata, Languages, and Programming (pp. 1-12). Springer, Berlin, Heidelberg.

Nissenbaum, H. (2020). Privacy in context. Stanford University Press.

Shvartzshnaider, Y., Pavlinovic, Z., Balashankar, A., Wies, T., Subramanian, L., Nissenbaum, H., & Mittal, P. (2019, May). Vaccine: Using contextual integrity for data leakage detection. In The World Wide Web Conference (pp. 1702-1712).

Shvartzshnaider, Y., Apthorpe, N., Feamster, N., & Nissenbaum, H. (2019, October). Going against the (appropriate) flow: A contextual integrity approach to privacy policy analysis. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 7, No. 1, pp. 162-170).

Tschantz, M. C., Sen, S., & Datta, A. (2020, May). Sok: Differential privacy as a causal property. In 2020 IEEE Symposium on Security and Privacy (SP) (pp. 354-371). IEEE.

Viljoen, S. (forthcoming). Democratic data: A relational theory for data governance. Yale Law Journal.

Walzer, M. (2008). Spheres of justice: A defense of pluralism and equality. Basic books.

Zhang, W., Ohrimenko, O., & Cummings, R. (2020). Attribute Privacy: Framework and Mechanisms. arXiv preprint arXiv:2009.04013.

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August 22, 2021

Research update: to study the economy of personal data

I have not been writing here for some time because of strokes of good luck that have been keeping me busy.

I’ve been awarded a Social Behavioral and Economic Sciences (SBE) Post-Doctoral Research Fellowship (“SPRF” in total) by the National Science Foundation.

This is a lot of words to write out, but they sum up to a significant change in my research and role that I’m still adjusting to.

First, I believe this means that I am a social scientist of some kind. What kind? It’s not clear. If I could have my choice, it would be “economist”. But since Economics is a field widely known for gatekeeping, and I do not have an Economic degree, I’m not sure I can get away with this.

Nevertheless, my SPRF research project is an investigation into the economics of data (especially personal data) using methods that are build on those used in orthodox and heterodox economics.

The study of the economics of personal data is coming from my dissertation work and the ongoing policy research I’ve done at NYU School of Law’s Information Law Institute. Though my work has touched on many other fields — computer science and the design of information systems; sociology and the study of race and networked publics; philosophy and law — at the end of the day the drivers of “technology’s” impact on society are businesses operating according to an economic logic. This is something that everybody knows, but that few academic researchers are in a position to admit, because many of the scholars who think seriously about these issues are coming from other disciplines.

For better or for worse, I have trouble sticking to a tunnel of which I can’t see the intellectual daylight at the end.

So how can we study the economy of personal data?

I would argue — that this is something that most Economists will balk at — that the tools currently available to study this economy are insufficient for the task. Who am I to say such a thing? Nobody special.

But weighing in my favor is the argument that the even the tools used by Economists to study the macroeconomy are insufficient for the task. This point was made decisively by the 2008 Financial Crisis, which blindsided the economic establishment. One of the reasons why Economics failed was because the discipline had deeply entrenched oversimplified assumptions in their economic models. One of these was representative agent modeling, which presumed to model the enter economy with a single “representative agent” for a sector or domain. This makes the economist’s calculations easier but is clearly unrealistic, and indeed it’s the differences between agents that create much of the dynamism and pitfalls of the economy. Hence the rise in heterogeneous agent modeling (HAM), which is explicit about the differences between agents with respect to things like, for example, wealth, risk aversion, discount factor, level of education, and so on.

It was my extraordinary good fortune to find an entry into the world of HAM via the Econ-ARK software project (Carroll et al, 2018; Benthall and Seth, 2020), which needed a software engineer enthusiastic about open source scientific tools at a moment when I was searching for a job. Econ-ARK’s HAM toolkit, HARK, has come a long way since I joined the project in late 2019. And it still has quite a ways to go. But it’s been a tremendously rewarding project to be involved with, in no small part because it has been a hands-on introduction to the nitty-gritty of contemporary Economics methods.

It’s these tools which I will be extending with insights from my other work, which is grounded more in computer science and legal scholarship, in order to model the data economy. Naturally, the economy for personal data depends on the heterogeneity of consumers — it is those differences that make a difference between consumers that make the trade in personal information possible and relevant. And while there are many notational and conventional differences between the orthodox Economics methods and the causal Bayesian frameworks that I’ve worked in before, these methods in fact share a logical core that makes them commensurable.

I’ve mentioned both orthodox and heterodox economics. By this I mean to draw a distinction between the core of the Economics discipline, which in my understanding is still tied to rational expectations and general equilibria — meaning the idea that agents know what to expect from the market and act accordingly — and heterodox views that find these assumptions to be dangerously unrealistic. This is truly a sore spot for Economics. As the trenchant critiques of Mirowski and Nik-Kah (2017) reveal, these core assumptions commit Economists to many absurd conclusions; however, they are loathe to abandon them lest they lose the tight form of rigor which they have demanding to maintain a kind of standardization within the discipline. Rational expectations aligns economics with engineering disciplines, like control theory and artificial intelligence, which makes their methods more in-demand. Equilibrium theories give Economics a normative force and excuses when its predictions do not pan out. However, the 2008 Financial Crisis embarassed these methods, and now the emerging HAM techniqes include not only a broadened from of rational agent modeling, but also a much looser paradigm of Agent-Based Modeling (ABM) that allow for more realistic dynamics with boundedly rational agents (Bookstaber, 2017).

Today, the biggest forces in the economy are precisely those that have marshaled information to their advantage in a world with heterogeneous agents (Benthall and Goldenfein, 2021). Economic agents differ both horizontally — like consumers of different demographic categories such as race and sex — and vertically — as consumers and producers of information services have different relationships to personal data. As I explore in forthcoming work with Salome Viljoen (2021), the monetization of personal data has always been tied to the financial system, first via credit reporting, and later through the financialization of consumer behavior through digital advertising networks. And yet the macroeconomic impact of the industries that profit from these information flows, which now account for the largest global companies, is not understood because of disciplinary blinders that Economics has had for decades and is only now trying to shed.

I’m convinced the research is well motivated. The objection, which comes from my most well-meaning mentors, is that the work is too difficult or in fact impossible. Introducing heterogeneously bounded rationality into economic modeling creates a great deal of modeling and computational complexity. Calibrating, simulating, and testing such models is expensive, and progress requires a great deal of technical thinking about how to compute results efficiently. There are also many social and disciplinary obstacles to this kind of work: for the reasons discussed above, it’s not clear where this work belongs.

However, I consider myself immensely fortunate to have a real, substantive, difficult problem to work on, and enough confidence from the National Science Foundation that they support my trying to solve it. It’s an opportunity of a lifetime and, to be honest, as a researcher who has often felt at the fringes of a viable scholarly career, a real break. The next steps are exciting and I can’t wait to see what’s around the corner.

References

Benthall, S., & Goldenfein, J. (2021, May). Artificial Intelligence and the Purpose of Social Systems. In Proceedings of the 2021 AAAI/ACM Conference on AI Ethics and Society (AIES’21).

Benthall, S., & Seth, M. (2020). Software Engineering as Research Method: Aligning Roles in Econ-ARK.

Benthall, S. & Viljoen, S. (2021) Data Market Discipline: From Financial Regulation to Data Governance. J. Int’l & Comparative Law https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774418

Bookstaber, R. (2017). The end of theory. Princeton University Press.

Carroll, C. D., Kaufman, A. M., Kazil, J. L., Palmer, N. M., & White, M. N. (2018). The Econ-ARK and HARK: Open Source Tools for Computational Economics. In Proceedings of the 17th Python in Science Conference (pp. 25-30).

Mirowski, P., & Nik-Khah, E. (2017). The knowledge we have lost in information: the history of information in modern economics. Oxford University Press.

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March 15, 2021

Luhmann, Social Systems, Cha. 1 § I

Niklas Luhmann (1927-1998) was a German sociologist who aimed to understand society in terms of systems theory.

I am reading Luhmann’s Social Systems (1995) because I have a hunch that this theory is relevant to my research. This post contains notes about Chapter 1, section I-II.

Often, scientists need to sacrifice intelligibility for accuracy. Luhmann is a scientist. He is unapologetic about this. He opens his “Instead of a Preface to the English Edition” (actual title) with:

“This is not an easy book. It does not accommodate those who prefer a quick and easy read, yet do not want to die without a taste of systems theory. This holds for the German text, too. If one seriously undertakes to work out a comprehensive theory of the social and strives for sufficient conceptual precision, abstraction and complexity in the conceptual infrastructure are unavoidable.”

Why bother reading such a difficult book? Why be a scientist and study social systems?

One reason to study society scientifically is to design and build better smart digital infrastructure.

Most people designing and building smart digital infrastructure today are not studying Luhmann. They are studying computer science. That makes sense: computer science is a science of smart digital artifacts. What has become increasingly apparent in recent years is that smart digital infrastructure is having an impact on society, and that the infrastructure is often mismatched to its social context. These mismatches are often considered to be a problem. Hence, a science of society might inform better technical designs.

§I

Chapter 1 opens with:

The following considerations assume that there are systems. Thus they do not begin with epistemological doubt. They also do not advocate a “purely analytical relevance” for systems theory. The most narrow interpretation of systems theory as a mere method of analyzing reality is deliberately avoided. Of course, one must never confuse statements with their objects; one must realize that statements are only statements and that scientific statements are only scientific statements. But, as least in systems theory, they refer to the real world. Thus the concept of system refers to something that is in reality a system and thereby incurs the responsibility of testing its statements against reality.

This is a great opening. It is highly uncommon for work in the social sciences to begin this way. Today, social science is almost always taught in theoretically pluralistic way. The student is taught several different theories of the same phenomenon. As they specialize into a social scientific discipline, they are taught to reproduce that discipline by citing its canonical thinkers and apply its analytical tools to whatever new phenomenon presents itself.

Not so with Luhmann. Luhmann is trying to start from a general scientific theory — systems theory — that in principle applies to physical, biological, and other systems, and to apply it to social systems. He cites Talcott Parsons, but also Herbert Simon, Ludwig von Bertalanffy, and Humberto Maturana. Luhmnn is not interested in reproducing a social scientific field; he is interested in reproducing the scientific field of systems theory in the domain of social science.

So the book is going to:

Be about systems theory in general
Address how social systems are a kind of system
Address how social systems relate to other kinds of system

There is a major challenge to studying this book in 2021. That challenge is that “systems theory” is not a mainstream scientific field today, and that people that do talk about “systems” normally do so in the context of “systems engineering”, to study and design industrial processes for example. They have their own quantitative discipline and methodologies that has little to do with sociology. Computer scientists, meanwhile, will talk about software systems and information systems, but normally in a way that has nothing to do with “systems theory” or systems engineering in a mechanical sense. Hazarding a guess, I would say that this has something to do with the cybernetics/AI split in the second half of the 20th century.

There is now a great deal of convergence in mathematical notation and concepts between different STEM fields, in part because much of the computational tooling has become ubiquitous. Computational social science has made great strides in recent years as a result. But many computational social science studies apply machine learning techniques to data generated by a social process, despite the fact that nobody believes the model spaces used in machine learning contain a veridical model of society.

This has led to many of the ethical and social problems with “AI”. Just for brief example, it is well known that estimating fitness via regression for employment or parole from personal information is, even when sensitive categories are excluded, likely to reproduce existing societal biases extant in the data through proxy variables in the feature set. A more subtle causal analysis can perhaps do better, but the way causality works at a societal level is not straightforward. See Lily Hu’s discussion of this topic, for some deeper analysis. Understanding the possible causal structures of society, including the possibility of “bottom-up” emergent effects and “downward causation” effects from social structures, would potentially improve the process of infrastructure design, whether manual or automated (via machine learning).

With this motive in mind, we will continue to slowly analyze and distill Luhmann in search for relevant insights.

For Luhmann, “systems theory … claims universal validity for everything that is a system.” Implicitly, systems theory has perfect internal validity. Luhmann expresses this theory in German, originally. But it really feels like there should be a mathematization of this work. He does not cite one yet, but the spoiler is that he’s eventually going to use George Spencer-Brown’s Laws of Form. For reasons I may get into later if I continue with this project, I believe that’s an unfortunate choice. I may have to find a different way to do the mathematization.

Rather, he follows through on his commitment to the existence of real systems by inferring some necessary consequences of that first principle. He is not content with a mathematical representation; systems theory must have “a real reference to the world”; “it is forced to treat itself as one of its objects in order to compare itself with others among those objects”. The crux is that systems theory, being a system itself, has to be able to take itself into account from the start. Hence, the commitment to real systems entails the realness of self-referential systems. “This means … there are systems that have the ability to establish relations with themselves and to differentiate these relations from relations with their environment.”

We are still in §I, which is itself a sort of preamble situating systems theory as a scientific theory, but already Luhmann is exposing the substance of the theory; in doing so, he demonstrates how truly self-referential — and consistently so — systems theory is. As he’ll say more definitively later, one essential feature of a system is that it is different from its environment. A system has, in effect, an “inside”, and also an “outside”. Outside the system is the environment. The part of the system that separates the inside of the system and its environment is the boundary. This binary aspect of the system (the system, and the not-the-system (environment)) clarifies the logic of ‘self-reference’. Self-referential systems differentiate between themselves and not-themselves.

So far, you have perhaps noted that Luhmann is a terribly literal writing. It is no surprise that the focus of his book, Social Systems, is that subset of systems that are “social”. What are these systems like? What makes them different from organisms (also systems), or systems of machines? Luhmann eschews metaphor — a bold choice. “[W]e do not choose the shortcut of analogy, but rather the longer path of generalization and respecification.” We don’t want to be misled by analogies.

“Above all, we will have to emphasize the nonpsychic character of social systems.”

That’s something Luhmann says right after saying he doesn’t want to use metaphors when talking about social systems. What can this possibly mean? It means, among other things, that Luhmann is not interested in anybody’s subjective experience of a society as an account of what a social system is. A “psychic system”, like my lived experience, or yours, is not the same thing as the social system — though, as we will later read, psychic systems are “structurally coupled” with the social system in important ways. Rather, the social system is constituted, objectively, by the communications between people. This makes it a more ready object of science.

It is striking to me that Luhmann is not more popular among analysts of social media data, because at least superficially he seems to be arguing, in effect, the social system of Twitter is not the system of Twitter’s users. Rather, it’s the system of the tweets. That’s one way of looking at things, for sure. Somewhat abashedly, I will say that Luhmann is an interesting lens through which to view Weird Twitter, which you may recall as a joke-telling subculture of Twitter that was popular before Former President Trump made Twitter much, much weirder. I think there’s some interesting comparisons to be drawn between Anthony Cohen’s theory of the symbolic construction of community, complete with symbolic boundary, and Luhmann’s notion of the boundary of a social system. But I digress.

Luhmann hasn’t actually used the word “communication” yet. He instead says “social contact”. “Every social contact is understood as a system, up to and including society as the inclusion of all possible contacts.” Possible contacts. Meaning that the system is defined in part by its unrealized but potential states. It can be stochastic; it can be changing its internal states to adapt to the external environment. “In other words, the general theory of social systems claims to encompass all sociology’s potential topics and, in this sense, to be a universal sociological theory.” Universal sociological theories are terribly unpopular these days. But Luhmann attempted it. Did he succeed?

“Yet, a claim to universality is not a claim to exclusive correctness, to the exclusive validity, and thus necessity (noncontingency), of one’s own account.” Nobody claiming to have a universal theory does this. Indeed, a theory learns about its own contingency through self-reference. So, social systems theory discovers it European origins, for example, as soon as it considers itself. What then? At that point, one “distinguish[es] between claims of universality and claims to exclusivity”, which makes utter sense, or “by recognizing that structural contingencies must be employed as an operative necessity, with the consequence that there is a constant contingency absorbtion through the successes, practices, and commitments in the scientific system.”

Contingency absorbtion is a nice idea. It is perhaps associated with the idea of abstraction: as one accumulates contingent experiences and abstracts from them, one discovers necessary generalities which are true for all contingent experiences. This has been the core German philosophical method for centuries, and it is quite powerful. We seem to have completely forgotten it in the American academic system. That is why the computer scientists have taken over everything. They have a better universalizing science than the sociologists do. Precisely for that reason, we are seeing computational systems in constant and irksome friction with society. American sociologists need to stop insisting on theoretical pluralism and start developing a universal sociology that is competitive, in terms of its universality, with computer science, or else we will never get smart infrastructure and AI ethics right.

References

Luhmann, N. (1995). Social systems. Stanford University Press.

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February 21, 2021

Social justice, rationalism, AI ethics, and libertarianism

There has been a lot of drama on the Internet about AI ethics, and that drama is in the mainstream news. A lot of this drama is about the shakeup in Google’s AI Ethics team. I’ve already written a little about this. I’ve been following the story since, and I would adjust my emphasis slightly. I think the situation is sadder than I did at first. But my take (and I’m sticking to it) is that what’s missing from the dominant narrative in that story, that it is about race and gender representation in tech, (a view quite well articulated in academic argot by Tao and Varshney (2021), by the way) is an analysis of the corporate firm as an organization, what it means to be an employee of a firm, and the relationship between firms and artificial intelligence.

These questions about the corporate firm are not topics that get a lot of traction on the Internet. The topics that get a lot of traction on the Internet are race and gender. I gather that this is something anybody with a blog, (let alone publishing news), discovers. I write this blog for an imagined audience of about ten people. It is mainly research notes to myself. But for some reason, this post about racism got 125 views this past week. For me, that’s a lot of views. Who are those people?

The other dramatic thing going in my corner of the Internet this past week is also about race and gender, and also maybe AI ethics. It is the reaction to Cade Metz’s NYT article “Silicon Valley’s Safe Space“, which is about the blog Slate Star Codex (SSC), rationalist subculture, libertarianism and … racism and sexism.

I learned about this kerfuffle in a backwards way. I learned about it through Glen Weyl’s engagement with SSC about technocracy, which I suppose he was bumping to ride on the shockwave created by the NYT article. In 2019, Weyl posted a critique of “technocracy” which was also a rather pointed attack on the rationalism community, in part because of its connection to “neoreaction”. SSC responded rather adroitly, in my opinion; Weyl responded. It’s an interesting exchange about political theory.

I follow Weyl on Twitter because I disagree with him rather strongly on niche topics in data regulation. As a researcher, I’m all about these niche topics in data regulation; I think they are where the rubber really hits the road on AI ethics. Weyl has published the view that consumer internet data should be treated as a form of labor. In my corner of technology policy research, which is quite small in terms of its Internet footprint, we think this is nonsense; it is what Salome Viljoen calls a propertarian view of data. The propertarian view of data is, for many important reasons, wrong. “Markets are never going to fix the AI/data regulation problem. Go read some Katharina Pistor, for christ’s sake!” That’s the main thing I’ve been waiting to say to Weyl, who has a coveted Microsoft Research position, a successful marketing strategy for his intellectual brand, perfect institutional pedigree, and so on.

Which I mean to his credit: he’s a successful public intellectual. This is why I was surprised to see him tweeting about rationalist subculture, which is not, to me, a legitimate intellectual topic, sanctioned as part of the AI/tech policy/whatever research track. My experiences with rationalism have all been quite personal and para-academic. It is therefore something of a context collapse — the social media induced bridging of social spheres — for me personally. (Marwick and Boyd, 2011; Davis and Jurgenson, 2014)

Context collapse is what it’s all about, isn’t it? One of the controversies around the NYT piece about SSC was that the NYT reporter, Metz, was going to reveal the pseudonymous author of SSC, “Scott Alexander”, as the real person Scott Siskind. This was a “doxxing”, though the connection was easy to make for anybody looking for it. Nevertheless, Siskind initially had a strong personal sense of his own anonymity as a writer. Siskind is, professionally, a psychotherapist, which is a profession with very strong norms around confidentiality and its therapeutic importance. Metz’s article is about this. It is also about race and gender — in particular the ways in which there is a therapeutic need for a space in which to discuss race and gender, as well as other topics, seriously, but also freely, without the major social and professional consequences that have come to be associated with it.

Smart people are writing about this and despite being a “privacy scholar”, I’m not sure I can say much that is smarter. Will Wilkinson’s piece is especially illuminating. His is a defense of journalism and a condemnation of what, I’ve now read, was an angry Internet mob that went after Metz in response to this SSC doxxing. It is also an unpacking of Siskind’s motivations based on his writing, a diagnosis of sorts. Spiers has a related analysis. A theme of these arguments against SSC and the rationalists is, “You weren’t acting so rationally this time, were you?”

I get it. The rationalists are, by presenting themselves at times as smarter-than-thou, asking for this treatment. Certainly in elite intellectual circles, the idea that participation in a web forum should give you advanced powers of reason that are as close as we will ever get to magic is pretty laughable. What I think I can say from personal experience, though, is that elite intellectuals seriously misunderstand popular rationalism if they think that it’s about them. Rather, popular rationalism is a non-elite social movement that’s often for people with non-elite backgrounds and problems. Their “smarter-than-thou” was originally really directed at other non-elite cultural forms, such as Christianity (!). I think this is widely missed.

I say this with some anecdata. I lived in Berkeley for a while in graduate school and was close with some people who were bona fide rationalists. I had an undergraduate background from an Ivy League university in cognitive science and was familiar with the heuristics and biases program and Bayesian reasoning. I had a professional background working in software. I should have fit in, right? So I volunteered twice at the local workshop put on by the Center for Applied Rationality to see what it was all about.

I noticed, as one does, that it’s mostly white men at these workshops and when asked for feedback I pointed this out. Eager to make use of the sociological skills I was learning in grad school, I pointed out that if they did not bring in more diversity early on, then because of homophily effects they may never reach a diverse audience.

At the time, the leadership of CFAR told me something quite interesting. It was that they had looked at their organizational goals and capacities and decided that where they could make the most impact was on teaching the skills of rational thought to smart people from, say, the rural midwest U.S.A. who would otherwise not get the exposure to this kind of thinking or what I would call a community of practice around it. Many of these people (much like Elizabeth Spiers, according to her piece) come from conservative and cloistered Christian backgrounds. Yudkowsky’s Harry Potter and the Methods of Rationality is their first exposure to Bayesian reasoning. They are often the best math students in their homogeneous home town, and finding their way into an engineering job in California is a big deal, as is finding a community that fills an analogous role to organized religion but does not seem so intellectually backwards. I don’t think it’s accidental the Julia Galef, who founded CFAR, started out in intellectual atheist circles before becoming a leader in rationalism. Providing an alternative culture to Christianity is largely what popular rationalism is about.

From this perspective, it makes more sense why Siskind has been able to cultivate a following by discussing cultural issues from a centrist and “altruistic” perspective. There’s a population in the U.S. that grew up in conservative Christian settings, now makes a living in a booming technology sector whose intellectual principles are at odds with those of their upbringing, is trying to “do the right thing” and, being detached from political institutions or power, turns to the question of philanthropy, codified into Effective Altruism. This population is largely comprised of white guys who may truly be upwardly mobile because they are, relative to where they came from, good at math. The world they live in, which revolves around AI, is nothing like the one they grew up in. These same people are regularly confronted by a different ideology, a form of left wing progressivism, which denies their merit, resents their success, and considers them a problem, responsible for the very AI harms that they themselves are committed to solving. If I were one of them, I, too, would want to be part of a therapeutic community where I could speak freely about what was going on.

This is several degrees removed from libertarian politics, which I now see as the line connecting in Weyl. Wilkinson makes the compelling case for contemporary rationalism originating in Tyler Cohen’s libertarian economist blogging and the intellectual environment at George Mason University. This spins out Robin Hanson’s Overcoming Bias blog, which spins out Yudkowsky’s LessWrong forum, which is where popular rationalism incubated. Weyl is an east coast libertarian public intellectual and it makes sense that he would engage other libertarian public intellectuals. I don’t think he’s going to get very far picking fights on the Internet with Yudkowsky, but I could be wrong.

Weyl’s engagement with the rationalist community does highlight for me two other missing elements in the story-as-told-so-far, from my readings on it. I’ve been telling a story partly about geography and migration. I think there’s also an element of shifting centers of cultural dominance. Nothing made me realize that I am a parochial New Yorker like living in California for five years. Rationalism remains weird to me because it is, today, a connection between Oxford utilitarian philosophers, the Silicon Valley nouveau riche, and to some extent Washington, D.C.-based libertarians. That is a wave of culture bypassing the historical intellectual centers of northeast U.S. Ivy League universities which for much of America’s history dominated U.S. politics.

To some extent, this speaks to the significance of the NYT story as well. It was not the first popular article about rationalists; Metz mentions the TechCrunch article about neoreactionaries (I’ll get to that) but not the Sam Frank article in Harpers, “Come With Us If You Want To Live” (2015), which is more ethnographic in its approach. I think it’s a better article. But the NYT has a different audience and a different standard for relevance. NYT is not an intellectual literary magazine. It is the voice of New York City, once the Center of the Universe. New York City’s perspective is particularly weighty, relevant, objective, powerful because of its historic role as a global financial center and marketing center. When the NYT notices something, for a great many people, it becomes real. NYT is at the center of a large public sphere with a specific geographic locus, in a way that some blogs and web forums are not. So whether it was justified or not, Metz’s doxing of Siskind was a significant shift in what information was public, and to whom. Part of its significance is that it was an assertion of cultural power by an institution tied to old money in New York City over a beloved institution of new money in Silicon Valley. In Bourdieusian terms, the article shifted around social and cultural capital in a big way. Siskind was forced to make a trade by an institution more powerful than him. There is a violence to that.

This force of institutional power is perhaps the other missing element in this story. Wilkinson and Frank’s pieces remind me: this is about libertarianism. Weyl’s piece against technocracy is also about libertarianism, or maybe just liberalism. Weyl is arguing that rationalists, as he understands them, are libertarians but not liberals. A “technocrat” is somebody who wants to replace democratic governance mechanisms, which depend on pluralistic discourse, with an expert-designed mechanism. Isn’t this what Silicon Valley does? Build Facebook and act like it’s a nation? Weyl, in my reading, wants an engaged pluralistic public sphere. He is, he reveals later, really arguing with himself, reforming his own views. He was an economist, coming up with mathematical mechanisms to improve social systems through “radical exchange”; now he is a public intellectual who has taken a cultural turn and called AI an “ideology”.

On the other end of the spectrum, there are people that actually would, if they could, build an artificial island and rule it via computers like little lords. I guess Peter Thiel, who plays a somewhat arch-villain role in this story, is like this. Thiel does not like elite higher education and the way it reproduces the ideological conditions for a pluralistic democracy. This is presumably why he backs Curtis Yarvin, the “neoreactionary” writer and “Dark Enlightenment” thinker. Metz goes into detail about this, and traces a connection between Yarvin and SSC; there’s leaked emails about it. To some people, this is the real story. Why? Because neoreaction is racist and sexist. This, not political theory, I promise you, is what is driving the traffic. It’s amazing Metz didn’t use the phrase “red pill” or “alt-right” because that’s definitely the narrative being extended here. With Trump out of office and Amazon shutting down Parler’s cloud computing, we don’t need to worry about the QAnon nutcases (which were, if I’m following correctly, a creation of the Mercers) but what about the right wing elements in the globally powerful tech sector, because… AI ethics! There’s no escape.

Slate Star Codex was a window into the Silicon Valley psyche. There are good reasons to try and understand that psyche, because the decisions made by tech companies and the people who run them eventually affect millions.
And Silicon Valley, a community of iconoclasts, is struggling to decide what’s off limits for all of us.
At Twitter and Facebook, leaders were reluctant to remove words from their platforms — even when those words were untrue or could lead to violence. At some A.I. labs, they release products — including facial recognition systems, digital assistants and chatbots — even while knowing they can be biased against women and people of color, and sometimes spew hateful speech.
Why hold anything back? That was often the answer a Rationalist would arrive at.

Metz’s article has come under a lot of criticism for drawing sweeping thematic links between SSC, neoreaction, and Silicon Valley with very little evidence. Noah Smith’s analysis shows how weak this connection actually is. Silicon Valley is, by the numbers, mostly left-wing, and mostly, by the numbers, not reading rationalist blogs. Thiel, and maybe Musk, are noteworthy exceptions, not the general trend. What does any of this have to do with, say, Zuckerburg? Not much.

The trouble is that if the people in Silicon Valley are left-wing, then there’s nobody to blame for racist and sexist AI. Where could racism and sexism in AI possibly come from, if not some collective “psyche” of the technologists? Better, more progressive leaders in Silicon Valley, the logic goes, would lead to better social outcomes. Pluralistic liberalism and proper demographic representation would, if not for the likes of bad apples like Thiel, steer the AI Labs and the big tech companies that use their products towards equitability and justice.

I want to be clear: I think that affirmative action for under-represented minorities (URMs) in the tech sector is a wonderful thing, and that improving corporate practices around their mentorship, etc. is a cause worth fighting for. I’m not knocking any of that. But I think the idea that this alone will solve the problems of “AI ethics” is a liberal or libertarian fantasy. This is because assuming that the actions of a corporation will have the politics of its employees is a form of ecological fallacy. Corporations do not work for their employees; they work, legally and out of fiduciary duty, to their shareholders. And the AI operated at the grand social scales that we are talking about are not controlled by any one person; they are created and operated corporately.

In my view, what Weyl (who I probably agree with more than I don’t), the earlier libertarian bloggers like Hanson, the AI X-risk folks like Yudkowsky, and the popular rationalist movement all get wrong is the way institutional power necessarily exceeds that of individuals in part because and through of “artificial intelligence”, but also through older institutions that distribute economic and social capital. The “public sphere” is not a flat or radical “marketplace of ideas”; it is an ecology of institutions like the New York Times, playing on ideological receptiveness grounded in religious and economic habitus.

Jeffrey Friedman is a dedicated intellectual and educator, who for years has been a generous intellectual mentor and facilitator of political thought. Friedman, like Weyl, is a critic of technocracy. In an early intellectual encounter that was very formative for me, he invited me to write a book review of Philip Tetlock Expert Political Judgment for his journal. In 2007, it was my first academic publication. The writing is embarrassing and I’m glad it is behind a paywall. In the article, I argue against Friedman and for technocracy based on the use of artificial intelligence. I have been in friendly disagreement on this point with Friedman ever since.

The line of reasoning from Friedman’s book, Power without Knowledge: A Critique of Technocracy (2019), that I find most interesting is about the predictability of individuals. Is it possible for a technocrat to predict society? This question has been posed by many different social theorists. I believe Friedman is unique in suggesting that (a) individuals cannot be predicted by a technocrat because (b) of how individual behavior is determined by their ideas, which are sourced so variously and combined in such complex ways that they cannot be captured by an external or generalizing observer. The unpredictability of society is an objective obstacle to the social scientific prediction that is required for technocratic policies to function effectively and as intended. Instead of a technocracy, Friedman advocates for an “Exitocracy”, based on the idea of Exit from Hirschman, which prioritizes a robust private sector in which citizens can experiment and find happiness, over technocratic or what others might call paternalist public policy. Some of the attractiveness of this model is that it depends on minimal assumptions about the rationality of agents, and especially the agency of technocrats, but still achieves satisficing results. Friedman’s exitocracy is, he argues, a ‘judicious’ technocracy, calibrated to realistic levels of expertise and public ignorance. In allowing for redistribution and centralized governance of some public goods, Friedman’s exitocracy stands as an alternative to more radically libertarian “Exit”-oriented proposals such as those of Srinivasan, which have been associated with Silicon Valley and, by dubious extension, the rationalist community.

At this time, I continue to disagree with Friedman. Academics are particularly unpredictable with respect to their ideas, especially if they do not value prestige or fame as much as their other colleagues. But most people are products of their background, or their habits, or their employers, or their social circles, or their socially structured lived experience. Institutions can predict and control people, largely by offering economic incentives. This predictability is what has made AI effective commercially — in uses by advertisers, for example — and it was what makes centralize public policy, or technocracy, possible.

But I could be wrong about this point. It is an empirical question how and under what conditions people’s beliefs and actions are unpredictable given background conditions. This amount of variability, which we might consider a kind of “freedom”, is a condition for whether technocracy, and AI, is viable or not. Are we free?

References

Davis, J. L., & Jurgenson, N. (2014). Context collapse: Theorizing context collusions and collisions. Information, communication & society, 17(4), 476-485.

Friedman, J. (2019). Power without knowledge: a critique of technocracy. Oxford University Press.

Kollman, K., Miller, J. H., & Page, S. E. (1997). Political institutions and sorting in a Tiebout model. The American Economic Review, 977-992.

Marwick, A. E., & Boyd, D. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New media & society, 13(1), 114-133.

Pistor, K. (2020). Rule by data: The end of markets?. Law & Contemp. Probs., 83, 101.

Tao, Y., & Varshney, K. R. (2021). Insiders and Outsiders in Research on Machine Learning and Society. arXiv preprint arXiv:2102.02279.

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February 16, 2021

Crypto, macro, and information law

Dogecoin is in the news this week because of Elon Musk’s pump and dump in the latest of notable asset bubbles fueled in small part by Internet-informed, perhaps frivolous, day-traders. The phenomenon reminds me of this curious essay about viral art. It concludes:

The doge meme is a Goldsmithian piece, passing ephemerally through a network of peers. In a LaBeoufian moment, Jackson Palmer invented Dogecoin, capturing the meme and using it to leverage networks of power. Now it is art post-LaBeouf in its greatest form: authorless art as economic power, transmitted over networks. As the synthesized culmination of the traditions of economics and Western literature, DogeCoin is one of the greatest achievements in the history of art, if not the greatest.

This paragraph is perhaps best understood, if at all, as an abstruse joke. The essay itself is most likely not written by “Niklos Szabo”, easily conflated with Nick Szabo, one of the deeper thinkers behind cryptocurrency more generally. The real Szabo has written much more seriously and presciently about culture and the economy. As an aside, I believe Szabo’s writings about book consciousness prefigure Hildebrandt’s work on the role of the printing press as a medium contributing to the particular character of text-driven law. However if the enduring success of cryptocurrencies validates Szabo’s economics more than his cultural theory. His 2002 paper “Shelling out: the origins of money” is a compelling history of currency. Notably, it is not a work of formal economic theory. Rather, it draws on historical and anthropological examples to get at the fundamentals of the role currency plays in society. This study leads to the conclusion that currency must be costly to created and transferable with relatively low transaction costs. Bitcoin, for example, was designed to have these qualities.

What Szabo does not discuss in “Shelling out” is the other thing Bitcoin is most known for, which is speculative asset bubble pricing. Cryptocurrency has lurched into the mainstream in fits of speculative enthusiasm followed by crashes and breakdowns. It is risky.

Salome Viljoen and I are writing about financial regulations as part of our “Data Market Discipline” project. One takeaway from this work is that the major financial regulations in the United States were responses to devastating financial crises, such as the Great Depression and the 2008 financial crisis, which were triggered by the collapse of an asset bubble. So while currency is an old invention and the invention of new currencies is interesting, the project of maintaining a stable financial system is a relatively more recent legal project and an unfinished one at that. It is so much more unfinished for cryptocurrencies, that are not controlled by a central banking system, than for national fiat currencies for which, for example, interest rates can be used as a calibrating tool.

These are not idle theoretical points. Rather, they are at the heart of questions surrounding the recovery of the economy from COVID-related setbacks. Money from stimulus checks going to people who have no reason to increase their consumption (cf. Carroll et al., 2020) is perhaps responsible for the influx of retail investment into equities markets and, in particular, Reddit-coordinated asset bubbles such as the ones we’re seeing recently with Gamestop and Dogecoin. The next stimulus package being prepared by the Biden administration has sounded alarms from parts of the Economics establishment that it will spur inflation, while Janet Yellen has argued that this outcome can be prevented using standard monetary policy tools such as the increase of interest rates. Arguably, the recent rising price of Bitcoin is due to this threat of macro-economic stability of the dollar-denominated financial system.

I don’t mean any of this conclusively. Rather, I’m writing this to register my growing realization that the myriad Internet effects on culture, economy, and the law are often much more motivated by movements in internationally coupled financial systems than “technology policy” specialists or “public interest technologists” are inclined to admit. We are inclined, because of our training in something else — whether it be computer science, environmental law, political philosophy, or whatever — to seek out metaphors from our own domain of expertise. But many of the most trenchant analyses of why the current technological landscape seems a bit off come down to failures of the price mechanism in the digital economy. I’m thinking of Kapczynski ‘s (2011) critique of the price mechanism in relation to intellectual property, and Strandburg’s (2013) analysis of the failure of pricing in on-line services. We have on the one hand the increasingly misconceptualized “Silicon Valley”‘s commitment to a “free market” and on the other hand few of the conditions under which a “free market” is classically considered to be efficient. The data economy does not meet even classically liberal (let alone New, more egalitarian, Liberal) standards of justice. And liberal legal theory is not equipped, Jake Goldenfein and I have argued, to grapple with this reality.

What progress can be made?

Maybe there is something somebody with enormous wealth or institutional power could do to change the situation. I’m not one of those people. However, there is some evidence to support the point that at the root of these problems is a conceptual, intellectual failing to understand what’s going on at the root of it.

In some recent work with Kathy Strandburg, we are examining the conceptual roots of the highly influential Law and Economics (L&E) branch of legal scholarship. This field absorbs the techniques of neoclassical economics and develops them into actionable policy proposals and legal rules of thumb. It has come under political criticism from the recently formed Law and Political Economy (LPE) movement. Interestingly, it has also been critique from a “Law and Macroeconomics” perspective, which argues that L&E should really be called “law and microeconomics”, because of its inability to internalize macroeconomic concepts such as the business cycle or changes in monetary policy.

Among the assumptions at the roots of L&E are notions of optimality and efficiency that make somewhat naive assumptions about the nature of price and money. For example, Kaldor-Hicks efficiency, a relaxation of Pareto efficiency used in welfare economics as applied to L&E, allows for transactions that alter the situations of agents so long as one agent, who gains, could theoretically compensate the other for their losses (see Feldman, 1998), . This concept is used to consider social welfare optimal, resolving the neoclassical problem of the incomparability of individual utilities through an implicit pricing mechanism. This leads L&E to favor “wealth maximizing” policies.

However, grounding legal theory in the idea of a robust price mechanism capable of subsuming all differences in individual preferences is quite naive in a digital economy that is always already at the intersection of many different currencies (including cryptocurrency), variable and politically vulnerable systems of credit and debt, and characterized by markets that do not that the legal scaffolding needed to drive them towards “true” prices. If Mirowski and Nik-Khah (2017) are correct and Economists have abandoned earlier notions of “truth” to faith in the market’s price as a “truth” derived from streams of information, something is indeed amiss. Data is not a commodity, and regulations that treat data flows as commodity exchanges not well matched to the reality. In the Hayekian model, price is the signal that combines available information. In the data economy, the complexity topology of real data flows belies simplistic views of “the market”.

What tech law needs is a new economic model, one that, just as general relativity in physics showed how classical mechanics was a special case of more complex universal laws, reveals how data, intellectual property, and price are connected in ways that go beyond the classical liberal imagination.

References

Benthall, Sebastian and Viljoen, Salome, Data Market Discipline: From Financial Regulation to Data Governance (January 27, 2021). J. Int’l & Comparative Law – (2021)

Carroll, C. D., Crawley, E., Slacalek, J., & White, M. N. (2020). Modeling the consumption response to the CARES Act (No. w27876). National Bureau of Economic Research.

Feldman, A. M. (1998). Kaldor-hicks compensation. The new Palgrave Dictionary of economics and the law, 2, 417-421.

Hildebrandt, M. (2015). Smart technologies and the end (s) of law: novel entanglements of law and technology. Edward Elgar Publishing.

Kapczynski, A. (2011). The cost of price: Why and how to get beyond intellectual property internalism. UCLA L. Rev., 59, 970.

Mirowski, P., & Nik-Khah, E. (2017). The knowledge we have lost in information: the history of information in modern economics. Oxford University Press.

Shekman, David. “Gamestop and the Surrounding Legal Questions.” Medium, Medium, 5 Feb. 2021, medium.com/@shekman27/gamestop-and-the-surrounding-legal-questions-fc0d1dc142d7.

Strandburg, K. J. (2013). Free fall: The online market’s consumer preference disconnect. U. Chi. Legal F., 95.

Szabo, N. (2002). Shelling out: the origins of money. Satoshi Nakamoto Institute.

Szabo, Niklos. “Art Post-LaBeouf.” Medium, Medium, 22 Sept. 2014, medium.com/@niklosszabo/art-post-labeouf-b7de5732020c.

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