Digifesto

Tag: Mireille Hildebrandt

double contingency and technology

One of the best ideas to come out of the social sciences is “double contingency”: the fact that two people engaged in communication are in a sense unpredictable to each other. That mutual unpredictability is an element of what it means to be in communication with another.

The most recent articulation of this idea is from Luhmann, who was interested in society as a system of communication. Luhmann is not focused on the phenomenology of the participants in a social system; in as sense, he looks like social systems the way an analyst might look at communications data from a social media site. The social system is the set of messages. Luhmann is an interesting figure in intellectual history in part because he is the one who made the work of Maturana and Varela officially part of German philosophical canon. That’s a big deal, as Maturana and Varela’s intellectual contributions–around the idea of autopoiesis, for example–were tremendously original, powerful, and good.

“Double contingency” was also discussed, one reads, by Talcott Parsons. This does not come up often because at some point the discipline of Sociology just decided to bury Parsons.

Double contingency comes up in interesting ways in European legal scholarship about technology. Luhmann, a dense German writer, is not read much in the United States, despite his being essentially right about things. Hildebrandt (2019) uses double contingency in her perhaps perplexingly framed argument for the “incomputability” of human personhood. Teubner (2006) makes a somewhat different but related argument about agency, double contingency, and electronic agents.

Hildebrandt and Teubner make for an interesting contrast. Hildebrandt is interested in the sanctity of humanity qua humanity, and in particular of privacy defined as the freedom to be unpredictable. This is an interesting inversion for European phenomenological philosophy. Recall that originally in European phenomenology human dignity was tied to autonomy, but autonomy depended on universalized rationality, with the implication that the most important thing about human dignity was that one followed universal moral rules (Kant). Hildebrandt is almost staking out an opposite position: that Arendtian natality, the unpredictableness of being an original being at birth, is the source of one’s dignity. Paradoxically, Hildebrandt argues that it humanity has this natality essentially and so claims that predictive technology might truly know the data subject are hubris, but also that the use of these predictive technologies is threat to natality unless their use is limited by data protection laws that ensure contestability of automated decisions.

Teubner (2006) takes a somewhat broader and, in my view, more self-consistent view. Grounding his argument firmly in Luhmann and Latour, Teubner is interested in the grounds of legally recognized (as opposed to ontologically, philosophically sanctified) personhood. And, he finds, the conditions of personhood can apply to many things besides humans! “Black box, double contingency, and addressability”, three fictions on which the idea of personhood depend, can apply to corporations and electronic agents as well as humans individually. This provides a kind of consistency and rationale for why we allow these kinds of entities to engage in legal contracts with each other. The contract, it is theorized, is a way of managing uncertainty, reducing the amount of contingency in the inherent “double contingency”-laden relationship.

Something of the old Kantian position comes through in Teubner, in that contracts and the law are regulatory. However, Teubner, like Nissenbaum, is ultimately a pluralist. Teubner writes about multiple “ecologies” in which the subject is engaged, and to which they are accountable in different modalities. So, the person, qua economic agent, is addressed in terms of their preferences. But the person, qua legal institutions, is addressed in terms of their embodiment of norms. The “whole person” does not appear in any singular ecology.

I’m sympathetic with the Teubnerian view here, perhaps in contrast with Hildebrandt’s view, the the following sense: while there may indeed be some intrinsic indeterminacy to an individual, this indeterminacy is meaningless unless it is also situated in (some) social ecology. However, what makes a person contingent visa vie one ecology is precisely that only a fragment of them is available to that ecology. The contingency to the first ecology is a consequence of their simultaneous presence within other ecologies. The person is autonomous, and hence also unpredictable, because of this multiplied, fragmented identity. Teubner, I think correctly, concludes that there is a limited form of personhood to non-human agents, but as these agents will be even more fragmented than humans, they are only persons in an attenuated sense.

I’d argue that Teubner helpfully backfills how personhood is socially constructed and accomplished, as opposed to guaranteed from birth, in a way that complements Hildebrandt nicely. In the 2019 article cited here, Hildebrandt argues for contestability of automated decisions as a means of preserving privacy. Teubner’s theory suggests that personhood–as participant in double contingency, as a black box–is threatened rather by context collapse, or the subverting of the various distinct social ecologies into a single platform in which data is shared ubiquitously between services. This provides a normative a universalist defense of keeping contexts separate (which in a different article Hildebrandt connects to purpose binding in the GDPR) which is never quite accomplished in, for example, Nissenbaum’s contextual integrity.

References

Hildebrandt, Mireille. “Privacy as protection of the incomputable self: From agnostic to agonistic machine learning.” Theoretical Inquiries in Law 20.1 (2019): 83-121.

Teubner, Gunther. “Rights of non‐humans? Electronic agents and animals as new actors in politics and law.” Journal of Law and Society 33.4 (2006): 497-521.

On Cilliers on “complex systems”

Mireille Hildebrandt has been surfacing the work of Cilliers on complex systems.

I’ve had a longstanding interest in the modeling of what are variously called “complex systems” or “complex adaptive systems” to study the formation of social structure, particularly as it might inform technical and policy design. I’m thrilled to be working on projects along these lines now, at last.

So naturally I’m intrigued by Hildebrandt’s use of Cillier’s to, it seems, humble if not delegitimize the aspirations of complex systems modeling. But what, precisely, is Cillier’s argument? Let’s look at the accessible “What can we learn from a theory of complexity?“.

First, what is a “complex system” to Cilliers?

I will not provide a detailed description of complexity here, but only summarize the general characteristics of complex systems as I see them.

1. Complex systems consist of a large number of elements that in
themselves can be simple.
2. The elements interact dynamically by exchanging energy or information. These interactions are rich. Even if specific elements only interact with a few others, the effects of these interactions are propagated throughout the system. The interactions are nonlinear.
3. There are many direct and indirect feedback loops.
4. Complex systems are open systems—they exchange energy or information with their environment—and operate at conditions far from
equilibrium.
5. Complex systems have memory, not located at a specific place, but
distributed throughout the system. Any complex system thus has a
history, and the history is of cardinal importance to the behavior of the
system.
6. The behavior of the system is determined by the nature of the interactions, not by what is contained within the components. Since the
interactions are rich, dynamic, fed back, and, above all, nonlinear, the
behavior of the system as a whole cannot be predicted from an inspection of its components. The notion of “emergence” is used to describe this aspect. The presence of emergent properties does not provide an argument against causality, only against deterministic forms of prediction.
7. Complex systems are adaptive. They can (re)organize their internal
structure without the intervention of an external agent.

Certain systems may display some of these characteristics more prominently than others. These characteristics are not offered as a definition of complexity, but rather as a general, low-level, qualitative description. If we accept this description (which from the literature on complexity theory appears to be reasonable), we can investigate the implications it would have for social or organizational systems.

This all looks quite standard at first glance except for point (6), which pointedly contains not only a description (the system exhibits emergent properties) but also a point about “deterministic forms of prediction”.

Cilliers hedges against actually defining his terms. This it seems, consistent with his position (expressed later).

The next section then presents some thematic, but tentative, consequences of thinking of organizations as complex systems. Presumably, this is all material he justifies elsewhere, as these are very loose arguments.

The point which Hildebrandt is gesturing towards is in the section “WHAT WE CANNOT LEARN FROM A THEORY OF COMPLEXITY”. This is Cilliers’s Negative Argument. There is indeed an argument here:

Looking at the positive aspects we discussed above, you will notice that none is specific. They are all heuristic, in the sense that they provide a general set of guidelines or constraints. Perhaps the best way of putting it is to say that a theory of complexity cannot help us to take in specific positions, to make accurate predictions. This conclusion follows inevitably from the basic characteristics discussed above.

At this point, Cilliers has twice (here, and in point (6) of the definition earlier) mentioned “prediction”, which is a very mathematically understood concept within the field of statistics. There is a red flag here. It is the use of the term “accurate predictions” or “deterministic forms of prediction”. What do these mean? Would a statistician say that any prediction is, strictly speaking, accurate or deterministic? Likely not. They could provide a confidence interval, or a Bayesian posterior odds–how much they would be willing to bet on an outcome. But these may not be what Cilliers means by “prediction”. There is a folk theoretic, qualitative sense of “prediction” which is sometimes used talismanicly by those who engage in foresight but eschew statistical prediction–scenario planners for example.

This is a different semantic world. On the formal modeling side, when one constructs a model and, as one does today, runs it as a simulation, what one does is run it multiple times over with different input parameters in order to get a sense of the probability distribution of outcomes. “Prediction”, to the formal science community, is the discovery of this distribution, not the pinpointing of a particular outcome. Very often, this distribution might have a high variance–meaning, a wide range of possible values, and therefore a very small amount of confidence that it will land on any particular. This is nevertheless considered a satisfactory outcome for a model. The model “predicts” that the result will be from a high-variance distribution.

For example, consider the toss of a single six-sided die. Nobody can predict the outcome deterministically. The “prediction” one can make is that it will land on 1, 2, 3, 4, 5, or 6, with equal odds.

So, already, we see Cilliers disconnected from mainstream statistical practice. If “deterministic prediction” is not what statisticians mean by prediction in any case, even simple ones, then they certainly do not believe they could make such a prediction about a complex system.

This is not the only time when Cilliers appears unfamiliar with the mathematical grounds that his argument gestures at. The following paragraph is quite disturbing:

In order to predict the behavior of a system accurately, we need a detailed understanding of that system, i.e., a model. Since the nature of a complex system is the result of the relationships distributed all over the system, such a model will have to reflect all these relationships. Since they are nonlinear, no set of interactions can be represented by a set smaller than the set itself—superposition does not hold. This is one way of saying that complexity is not compressible. Moreover, we cannot accurately determine the boundaries of the system, because it is open. In order to model a system precisely, we therefore have to model each and every interaction in the system, each and every interaction with the environment—which is of course also complex—as well as each and every interaction in the history of the system. In short, we will have to model life, the universe and everything. There is no practical way of doing this.

This paragraph’s validity hinges on its notion of “precision” which, as I’ve explained, is not something any statistically informed modeler is going for. A few comments on the terminology here:

  • In formal algorithmic information theory (which has, through Solomonoff induction and the minimum description length principle, a close underlying connection with statistical inference) “compressibility” refers to whether or not a representation of some kind–such as a written description or set of data–can be, given some some language of interpretation or computation, represented in a briefer or more compressed form. When one writes a computational description of a model, that is likely its most compressed form. (If not, it is not written very well). When the model is executed, it simulates the relations of objects within the system dynamically. These relations may well be non-linear. All of this is very conventional. If the model is stochastic–meaning, containing randomized behavior, as many are–then there will of course be a broad distribution of outcomes. And it’s true that any particular distribution will not be compressible to the source code alone: the compression will need to include also all the random draws used in making the stochastic decisions of the simulation. However, only the source code and the random draws will be needed. So it is still quite possible for the specific state of the model to be compressible to something much less than a full description of the state!
  • Typically, a model of a complex system will designate some objects and relations as endogenous, meaning driven by the internals of the model, and other factors as exogenous, meaning coming from outside of the boundary of the model. If the exogenous factors are unknown, and they almost always are, then they will be modeled as a set of all possible inputs, possibly with a probability distribution of some kind. This distribution can be attained through, for example, random sampling as well as adjusting it to take into account what is unknown. (Probability distributions that express that we don’t know about something are sometimes called “maximum entropy distributions”, for reasons that are clear from information theory.)

So in this paragraph, which seems to reflect the core part of Cilliers’s argument, it’s frankly, not clear that he knows what he’s talking about. The most charitable interpretation, I believe, is this: Cilliers is not satisfied with probabilistic prediction, as almost anybody else doing computational modeling of complex systems is bound to be. Rather, he believes the kind of prediction that matters is the prediction of a specific outcome with absolute certainty. This, truly, is not possible to get for a complex enough system. Indeed, even simple stochastic systems cannot be predicted in this way.

Let’s call this Cilliers’s Best Negative Argument. What are the implications of it?

What does this amount to in practice? It means that we have to make decisions without having a model or a method that can predict the exact outcome of those decisions. … Does this mean we should avoid decisions, hoping that they will make themselves? Most definitely not. … Not to make a decision is of course also a decision. What, then, are the nature of our decisions? Because we cannot base them on calculation only—calculation would eliminate the need for choice—we have to acknowledge that our decisions have an ethical nature.

This argument is, to a scientist, weird. Suppose, as is likely the case, that we can never make exact predictions but only, at best, probabilistic predictions. In other words, suppose all of our decisions are decisions under uncertainty, which is a claim anybody trained in, say, machine learning is bound to agree with for reasons that have nothing to do with Cilliers. Does this mean that: (a) these decisions cannot be based on calculation, and (b) these decisions have an ethical nature?

Prima facie, (a) is false. Decisions under uncertainty are made based on calculation all the time. This is what decision theory, a well-developed branch of mathematics and philosophy used in economics, for example, is all about. Simply: one calculates the action that maximizes the expected value of the action, meaning the value of the possible outcomes weighted according to their probability.

It is surprising that somebody writing about “complex systems” in the year 2000 working in the field of management science would not address this point, as the von Neumann-Morgenstern theory of utility was developed in 1947 and is not at all a secret.

Perhaps, then, Cilliers is downplaying this because his real mission is to revitalize the ethical. So far, it seems he is saying: decision-making under uncertainty is always, unlike decision-making under conditions of certainty, ethical in some sense. Is that what he’s saying?

I do not take it to mean being nice or being altruistic. It has nothing to do with middleclass values, nor can it be reduced to some interpretation of current social norms. I use the word in a rather lean sense: it refers to the inevitability of choices that cannot be backed up scientifically or objectively.

…. What?

Why call it ethics? First, because the nature of the system or organization in question is determined by the collection of choices made in it.

Ok, this looks fine.

Secondly, since there is no final objective or calculable ground for our decisions, we cannot shift the responsibility for the decision on to something else—“Don’t blame me, the genetic algorithm said we should sell!” We know that all of our choices to some extent, even if only in a small way, incorporate a step in the dark. Therefore we cannot but be responsible for them.

We are getting to the crux of the argument. Decision-making under uncertainty, Cilliers argues, carries responsibility.

There are two parts to this argument. The first is, I find, the most interesting. Decision-making within a complex system is much more difficult and existentially defining than decision-making about a complex system. And precisely because it is existentially defining, I could see how it would carry special responsibility, or ethical weight.

However, for the aforementioned reasons, his hinging this argument on calculability is confusing and uncompelling. There may be many situations where the most responsible or ethical decision is one based on calculated expected results. For example, consider the decision to implement an economic lockdown policy in response to a pandemic. One could listen to political interests of various stripes and appease one or the other. But perhaps in such a situation is it most responsible to calculate, to the best of one’s ability, the probably outcome of one’s choices before implementing them.

And it seems like Cilliers would agree with this:

It may appear at this stage as if I am arguing against any kind of calculation, that I am dismissing the importance of modeling complex systems. Nothing is further from the truth. The important point I want to make is that calculation will never be sufficient. The last thing this could mean is that calculation is unnecessary. On the contrary, we have to do all the calculation we possibly can. That is the first part of our responsibility as scientists and managers. Calculation and modeling will provide us with a great deal of vital information.

This is a point of happy agreement!

It will just not provide us with all the information.

This is a truism nobody doing computational modeling work would argue with.

The problem would remain, however, that this information has to be
interpreted.
All the models we construct—whether they are formal, mathematical models, or qualitative, descriptive models—have to be limited. We cannot model life, the universe, and everything. There may not be any
explicit ethical component contained within the model itself, but ethics (in the sense in which I use the term) has already played its part when the limits of the model were determined, when the selection was made of what would be included in the frame of the investigation. The results produced by the model can never be interpreted independently of that frame. This is no revelation, it is something every scientist knows, or at least should know. Unfortunately, less scrupulous people, often the popularizers of some scientific idea or technique, extend the field of applicability of that idea way beyond the framework that gives it sense and meaning.

Well, this is quite frustrating. It turns out Cilliers is not writing this article for scientists working on computational modeling of complex systems. He’s writing this article, I guess, to warn people off of listening to charlatans. This is a worthy goal. But then why would he write in a way that is so misleading about the nature of computational decision-making? Once again, the insight Cilliers is missing is that the difference between a deterministic model and a probabilistic model is not a difference that makes the latter less “calculable” or “objective” or “scientific”, even though it may (quantitatively) have less information about the system it describes.

Cilliers goes on:

My position could be interpreted as an argument that contains some mystical or metaphysical component, slipped in under the name “ethics.” In order to forestall such an interpretation, I will digress briefly. It is often useful to distinguish between the notions “complex” and “complicated.” A jumbo jet is complicated, a mayonnaise is complex (a least for the French). A complicated system is something we can model accurately (at least in principle). Following this line of thought, one may argue that the notion “complex” is merely a term we use for something we cannot yet model. I have much sympathy for this argument. If one maintains that there is nothing metaphysical about a complex system, and that the notion of causality has to be retained, then perhaps a complex system is ultimately nothing more than extremely complicated. It should therefore be possible to model complex systems in principle, even though it may not be practical.

In conversations about this material, it seems that some are under the impression that a difference between a “complicated” and a “complex” system is a difference in kind. It is clear from this paragraph that for Cilliers, this is not the case. This would accord with all the mathematical theory of complexity which would identify how levels of complexity can be quantitatively measured. Missing from this paragraph, still, is any notion of probability or statistical accuracy. Which is too bad.

In the end, my assessment is that Cilliers is making a good try here and if he’s influential, as I suppose he might by in South Africa, then he’s done so by popularizing some of the work of mathematicians, physicists, etc. But because of some key omissions, his argument is confusing if not misleading. In particular, it is prone to be misinterpreted, as it does not deal with precision about the underlying technical material. I would not rely on it to teach students about complex systems and the limitations of modeling them. I would certainly not draw any clear, ontological lines between “complicated” and “complex” systems as Cilliers does not do this himself.

References

Cilliers, Paul (2000). “What can we learn from a theory of complexity?” (PDF). Emergence2.1: 23-33. doi:10.1207/S15327000EM0201_03.

Is there hypertext law? Is there Python law?

I have been impressed with Hildbebrandt’s analysis of the way particular technologies provide the grounds for different forms of institutions. Looking into the work of Don Ihde, who I gather is a pivotal thinking in this line of reasoning, I find the ‘postphenomenological’ and ‘instrumental realist’ position very compelling. Lawrence Diver’s work on digisprudence, which follows in this vein, looks generative.

In my encounters with with work, I have also perceived there to be gaps and discrepancies in the texture of the argument. There is something uncanny about reading material that is, perceptually, almost correct. Either I am in error, or it is.

One key difference seems to be about the attitude towards mathematical or computational formalism. This is chiefly, I sense, truly an attitude, in the sense of emotional difference. Scholars in this area will speak, in personal communication, of being “wary” or “afraid”. It’s an embodied reaction which orients their rhetoric. It is shared with many other specifically legal scholars. In the gestalt of these arguments, the legal scholar will refer to philosophies of science and/or technology to justify a distance between lived reality, lifeworld, and artifice.

Taking a somewhat different perspective, there are other ways to consider the relationship between formalism, science, and fact, even when taking seriously the instrumental realist position. It is noteworthy, I believe, that this field of scholarship is so adamantly Latourian, and that Latour has succeeded in anathematizing Bourdieu. I now see more clearly how Science of Science and Reflexivity, which was both a refutation of Latour and a lament of how the capture of institutional power (such as nation-state provided research funding) is a distortion to the autonomous and legitimizing processes of science, are really all one argument. Latour, despite the wrongness of so much of his early work which is now so widely cited, became a powerful figure. The better argument will only win in time.

Bourdieu, it should be noted, is an instrumental realist about science, though he may not have been aware of Ihde and that line of discourse. He also saw the connection between formalism and instrumentation which seems to elude the postphenomenologist legal scholars. Formalism and instrumentation are both a form of practical “automation” which, if we take the instrumental realists seriously (and we should) wind up enabling the body, understood as perception-praxis, to see and know in different ways. Bourdieu, who obviously has read Foucault but improves on him, accepts the perception-praxis view of the body and socializes it through the concept of the habitus, which is key to his analysis of the sociology of science.

But I digress. What I have been working towards is the framing of the questions in the title. To recap, Hildebrandt, in my understanding, makes a compelling case for how the printing press, as a technology, has had specific affordances that have enabled the Rule of Law that is characteristic of constitutional democracy. This Rule of Law, or some descendent of it, remains dominant in Europe and perhaps this is why, via the Brussells Effect, the EU now stands as the protector of individuals from the encroaching power of machine-learning powered technologies, in the form of Information and Communication Infrastructure (ICI).

This is a fine narrative, though perhaps rather specifically motivated by a small number of high profile regulatory acts. I will not suggest that the narrative overplays anybody’s hand; it is useful as a schematic.

However, I am not sure the analysis is so solid. There seem to be some missing steps in the historical analysis. Which brings me to my first question, which is: what about hypertext? Hypertext is neither the text of the printing press, nor is it a form of machine learning. It is instrumentally dependent on scientific and technological formalism: the HyperText Markup Language (HTML) and the HyperText Transfer Protocol are both formal standards, built instrumentally on a foundation of computation and networking theory and technology. And as a matter of contemporary perception and praxis, it is probably the primary way in which people engage in analysis of law and communication about the law today.

So, what about it? Doesn’t this example show a contradiction at the heart of this instrumental realist legal scholarship?

The follow-up question is about another class of digital “languages”: software source code. Python, for example. These, even more than HyperText, are formalism, with semantics guaranteed by a compiler. But these semantics are in a sense legislated via the Python Enhancement Proposal process, and of course any particular Python application or software practice may be designed and mandated through a wide array of institutional mechanisms before being deployed to users.

I would look forward to work on these subjects coming from Hildebrandt’s CUHOBICOL research group, but for the fact that these technologies (which may bely the ideology motivating the project!) are excluded by the very system of categories the project invokes to classify different kinds of regulatory systems. According to the project web site (written, like all web sites, in HyperText), there are three (only three?) kinds of normativity: text-driven normativity, based in the printing press; data-based normativity, the normativity of feedback once based in cybernetic engineering and now based in machine learning; and code-based normativity. The last category is defined in terms of code’s immutability, which is rather alien to anybody who writes software code and has to deal with how it changes all the time. Moreover, the project’s aim is to explore code-based normativity through blockchain applications. I understand that gesturing at blockchain technology is a nice way to spice up a funding proposal. But by seeing normativity in these terms, many intermediate technologies, and therefore a broad technical design space of normative technology, are excluded from analysis.

The diverging philosophical roots of U.S. and E.U. privacy regimes

For those in the privacy scholarship community, there is an awkward truth that European data protection law is going to a different direction from U.S. Federal privacy law. A thorough realpolitical analysis of how the current U.S. regime regarding personal data has been constructed over time to advantage large technology companies can be found in Cohen’s Between Truth and Power (2019). There is, to be sure, a corresponding story to be told about EU data protection law.

Adjacent, somehow, to the operations of political power are the normative arguments leveraged both in the U.S. and in Europe for their respective regimes. Legal scholarship, however remote from actual policy change, remains as a form of moral inquiry. It is possible, still, that through professional training of lawyers and policy-makers, some form of ethical imperative can take root. Democratic interventions into the operations of power, while unlikely, are still in principle possible: but only if education stays true to principle and does not succumb to mere ideology.

This is not easy for educational institutions to accomplish. Higher education certainly is vulnerable to politics. A stark example of this was the purging of Marxist intellectuals from American academic institutions under McCarthyism. Intellectual diversity in the United States has suffered ever since. However, this was only possible because Marxism as a philosophical movement is extraneous to the legal structure of the United States. It was never embedded at a legal level in U.S. institutions.

There is a simply historical reason for this. The U.S. legal system was founded under a different set of philosophical principles; that philosophical lineage still impacts us today. The Founding Fathers were primarily influenced by John Locke. Locke rose to prominence in Britain when the Whigs, a new bourgeois class of Parliamentarian merchant leaders, rose to power, contesting the earlier monarchy. Locke’s political contributions were a treatise pointing out the absurdity of the Divine Right of Kings, the prevailing political ideology of the time, and a second treatise arguing for a natural right to property based on the appropriation of nature. This latter political philosophy was very well aligned with Britain’s new national project of colonialist expansion. With the founding of the United States, it was enshrined into the Constitution. The liberal system of rights that we enjoy in the U.S. are founded in the Lockean tradition.

Intellectual progress in Europe did not halt with Locke. Locke’s ideas were taken up by David Hume, whose introduced arguments that were so agitating that they famously woke Immanuel Kant, in Germany, from his “dogmatic slumber”, leading him to develop a new highly systematic system of morality and epistemology. Among the innovations in this work was the idea that human freedom is grounded in the dignity of being an autonomous person. The source of dignity is not based in a natural process such as the tilling of land. It is rather based in on transcendental facts about what it means to be human. The key to morality is treating people like ends, not means; in other words, not using people as tools to other aims, but as aims in themselves.

If this sound overly lofty to an American audience, it’s because this philosophical tradition has never taken hold in American education. In both the United Kingdom and Britain, Kantian philosophy has always been outside the mainstream. The tradition of Locke, through Hume, has continued on in what philosophers will call “analytic philosophy”. This philosophy has taken on the empiricist view that the only source of knowledge is individual experience. It has transformed over centuries but continues to orbit around the individual and their rights, grounded in pragmatic considerations, and learning normative rules using the case-by-case approach of Common Law.

From Kant, a different “continental philosophy” tradition produced Hegel, who produced Marx. We can trace from Kant’s original arguments about how morality is based on the transcendental dignity of the individual to the moralistic critique that Marx made against capitalism. Capitalism, Marx argued, impugns the dignity of labor because it treats it like a means, not an end. No such argument could take root in a Lockean system, because Lockean ethics has no such prescription against treating others instrumentally.

Germany lost its way at the start of the 20th century. But the post-war regime, funded by the Marshall plan, directed by U.S. constitutional scholars as well as repatriating German intellectuals, had the opportunity to rewrite their system of governance. They did so along Kantian lines: with statutory law, reflecting a priori rational inquiry, instead of empiricist Common Law. They were able to enshrine into their system the Kantian basis of ethics, with its focus on autonomy.

Many of the intellectuals influencing the creation of the new German state were “Marxist” in the loose sense that they were educated in the German continental intellectual tradition which, at that time, included Marx as one of its key figures. By the mid-20th century they had naturally surpassed this ideological view. However, as a consequence, the McCarthyist attack on Marxism had the effect of also purging some of the philosophical connection between German and U.S. legal education. Kantian notions of autonomy are still quite foreign to American jurisprudence. Legal arguments in the United States draw instead on a vast collection of other tools based on a much older and more piecemeal way of establishing rights. But are any of these tools up to the task of protecting human dignity?

The EU is very much influenced by Germany and the German legal system. The EU has the Kantian autonomy ethic at the heart of its conception of human rights. This philosophical commitment has recently expressed itself in the EU’s assertion of data protection law through the GDPR, whose transnational enforcement clauses have brought this centuries-old philosophical fight into contemporary legal debate in legal jurisdictions that predate the neo-Kantian legal innovations of Continental states.

The puzzle facing American legal scholars is this: while industrial advocates and representatives tend to disagree with the strength of the GDPR, arguing that it is unworkable and/or based on poorly defined principle, the data protections that it offer seem so far to be compelling to users, and the shifting expectations around privacy in part induced by it are having effects on democratic outcomes (such as the CCPA). American legal scholars now have to try to make sense of the GDPR’s rules and find a normative basis for them. How can these expansive ideas of data protection, which some have had the audacity to argue is a new right (Hildebrandt, 2015), be grafted onto the the Common Law, empiricist legal system in a way that gives it the legitimacy of being an authentically American project? Is there a way to explain data protection law that does not require the transcendental philosophical apparatus which, if adopted, would force the American mind to reconsider in a fundamental way the relationship between individuals and the collective, labor and capital, and other cornerstones of American ideology?

There may or may not be. Time will tell. My own view is that the corporate powers, which flourished under the Lockean judicial system because of the weaknesses in that philosophical model of the individual and her rights, will instinctively fight what is in fact a threatening conception of the person as autonomous by virtue of their transcendental similarity with other people. American corporate power will not bother to make a philosophical case at all; it will operate in the domain of realpolitic so well documented by Cohen. Even if this is so, it is notable that so much intellectual and economic energy is now being exerted in the friction around a poweful an idea.

References

Cohen, J. E. (2019). Between Truth and Power: The Legal Constructions of Informational Capitalism. Oxford University Press, USA.

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

Neutral, Autonomous, and Pluralistic conceptions of law and technology (Hildebrandt, Smart Technologies, sections 8.1-8.2)

Continuing notes and review of Part III of Hildebrandt’s Smart Technologies and the End(s) of Law, we begin chapter 8, “Intricate entanglements of law and technology”. This chapter culminates in some very interesting claims about the relationship between law and the printing press/text, which I anticipate provide some very substantive conclusions.

But the chapter warms up by a review of philosophical/theoretical positions on law and technology more broadly. Section 8.2. is structured as a survey of these positions, and in an interesting way: Hildebrandt lays out Neutral, Autonomous, and Pluralistic conceptions of both technology and law in parallel. This approach is dialectical. The Neutral and Autonomous conceptions are, Hildebrandt argues, narrow and naive; the Pluralistic conception captures nuances necessary to understand not only what technology and law are, but how they relate to each other.

The Neutral Conception

This is the conception of law and technology as mere instruments. A particular technology is not good or bad, it all depends on how it’s used. Laws are enacted to reach policy aims.

Technologies are judged by their affordances. The goals for which they are used can be judged, separately, using deontology or some other basis for the evaluation of values. Hildebrandt has little sympathy for this view: “I believe that understanding technologies as mere means amounts to taking a naive and even dangerous position”. That’s because, for example, technology can impact the “in-between” of groups and individuals, thereby impacting privacy by its mere usage. This echoes the often cited theme of how artifacts have politics (Winner, 1980): by shaping the social environment by means of their affordances.

Law can also be thought of as neutral instrument. In this case, it is seen as a tool of social engineering, evaluated for its effects. Hildebrandt says this view of law fits “the so-called regulatory paradigm”, which “reigns in policy circles, and also in policy science, which is a social science inclined to take an exclusively external perspective on the law”. The law regulates behavior externally, rather than the actions of citizens internally.

Hildebrandt argues that when law is viewed instrumentally, it is tempting to then propose that the same instrumental effects could be achieved by technical infrastructure. “Techno-regulation is a prime example of what rule by law ends up with; replacing legal regulation with technical regulation may be more efficient and effective, and as long as the default settings are a part of the hidden complexity people simply lack the means to contest their manipulation.” This view is aligned with Lessig’s (2009), which Hildebrandt says is “deeply disturbing”; as it is aligned with “the classical law and economics approach of the Chicago School”, it falls short…somehow. This argument will be explicated in later sections.

Comment

Hildebrandt’s criticism of the neutral conception of technology is that it does not register how technology (especially infrastructure) can have a regulatory effect on social life and so have consequences that can be normatively evaluated without bracketing out the good or bad uses of it by individuals. This narrow view of technology is precisely that which has been triumphed over by scholars like Lessig.

Hildebrandt’s criticism of the neutral conception of law is different. It is that by understanding law primarily by its external effects (“rule by law”) diminishes the true normative force of a more robust legality that sees law as necessarily enacted and performed by people (“Rule of Law”). But nobody would seriously think that “rule by law” is not “neutral” in the same sense that some people think technology is neutral.

The misalignment of these two positions, which are presented as if they are equivalent, obscures a few alternative positions in the logical space of possibilities. There are actually two different views of the neutrality of technology: the naive one that Hildebrandt takes time to dismiss, and the more sophisticated view that technology should be judged by its social effects just as an externally introduced policy ought to be.

Hildebrandt shoots past this view, as developed by Lessig and others, in order to get to a more robust defense of Rule of Law. But it has to be noted that this argument for the equivalence of technology and law within the paradigm of regulation has beneficial implications if taken to its conclusion. For example, in Deirdre Mulligan’s FAT* 2019 keynote, she argued that public sector use of technology, if recognizes as a form of policy, would be subject to transparency and accountability rules under laws like the Administrative Procedure Act.

The Autonomous Conception

In the autonomous conception of technology and law, there is no agent using technology or law for particular ends. Rather, Technology and Law (capitalized) act with their own abstract agency on society.

There are both optimistic and pessimistic views of Autonomous Technology. There is hyped up Big Data Solutionism (BDS), and dystopian views of Technology as the enframing, surveilling, overpowering danger (as in, Heidegger). Hildebrandt argues that these are both naive and dangerous views that prevent us from taking seriously the differences between particular technologies. Hildebrant maintains that particular design decisions in technology matter. We just have to think about the implications of those decisions in a way that doesn’t deny the continued agency involved the continuous improvement, operation, and maintenance of the technology.

Hildebrant associates the autonomous conception of law with legal positivism, the view of law as a valid, existing rule-set that is strictly demarcated from either (a) social or moral norms, or (b) politics. The law is viewed as legal conditions for legal effects, enforced by a sovereign with a monopoly on violence. Law, in this sense, legitimizes the power of the state. It also creates a class of lawyers whose job it is to interpret, but not make, the law.

Hildebrandt’s critique of the autonomous conception of law is that it gives the law too many blind spots. If Law is autonomous, it does not need to concern itself with morality, or with politics, or with sociology, and especially not with the specific technology of Information-Communications Infrastructure (ICI). She does not come out and say this outright, but the implication is that this view of Law is fragile given the way changes in the ICI are rocking the world right now. A more robust view of law would give better tools for dealing with the funk we’re in right now.

The Pluralistic Conception

The third view of technology and law, the one that Hildebrandt endorses, is the “pluralistic” or “relational” view of law. It does not come as a surprise after the exploration of the “neutral” and “autonomous” conceptions.

The way I like to think about this, the pluralistic conception of technology/law, is: imagine that you had to think about technology and law in a realistic way, unburdened by academic argument of any kind. Imagine, for example, a room in an apartment. Somebody built the room. As a consequence of the dimensions of the room, you can fit a certain amount of furniture in it. The furniture has affordances; you can sit at chairs and eat at tables. You might rearrange the furniture sometimes if you want a different lifestyle for yourself, and so on.

In the academic environment, there are branches of scholarship that like to pretend they discovered this totally obvious view of technology for the first time in, like, the 70’s or 80’s. But that’s obviously wrong. As Winner (1980) points out, when Ancient Greeks were building ships, they obviously had to think about how people would work together to row and command the ship, and built it to be functional. Civil engineering, transportation engineering, and architecture are fields that deal with socially impactful infrastructure, and they have to deal with the ways people react, collectively, to what was built. I can say from experience doing agile development of software infrastructure that software engineers, as well, think about their users when they build products.

So, we might call this the “realistic” view–the view that engineers, who are the best situated to understand the processes of producing and maintaining technology, since that’s their life, have.

I’ve never been a lawyer, but I believe one gets to the pluralistic, or relational, view of law in pretty much the same way. You look at how law has actually evolved, historically, and how it has always been wrapped up in politics and morality and ICI’s.

So, in these sections, Hildebrandt drives home in a responsible, scholarly way the fact that neither law nor technology (especially technological infrastructure, and especially ICI) are autonomous–they are historically situated creates of society–and nor are they instrumentally neutral–they do have a form of agency in their own right.As my comment above notes, to me the most interesting part of this chapter was the gaps and misalignment in the section on the Neutral Conception section. This conception seems most aligned with an analytically clear, normative conception of what law and technology are supposed to be doing, which is what makes this perspective enduringly attractive to those who make them. The messiness or the pluralistic view, while more nuanced, does not provide a guide for design.

By sweeping away the Neutral conception of law as instrumental, Hildebrandt preempts arguments that the law might fail to attain its instrumental goals, or that the goals of law might sometimes be attained through infrastructure. In other words, Hildebrandt is trying to avoid a narrow instrumental comparison between law and technology, and highlights instead that they are relationally tied to each other in a way that prevents either from being a substitute for the other.

References

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

Lessig, Lawrence. Code: And other laws of cyberspace. ReadHowYouWant. com, 2009.

Winner, Langdon. “Do artifacts have politics?.” Daedalus(1980): 121-136.

Antinomianism and purposes as reasons against computational law (Notes on Hildebrandt, Smart Technologies, Sections 7.3-7.4)

Many thanks to Jake Goldenfein for discussing this reading with me and coaching me through interpreting it in preparation for writing this post.

Following up on the discussion of sections 7.1-7.2 of Hildebrandt’s Smart Technologies an the End(s) of Law (2015), this post discusses the next two sections. The main questions left from the last section are:

  • How strong is Hildebrandt’s defense of the Rule of Law, as she explicates it, as worth preserving despite the threats to it that she acknowledges from smart technologies?
  • Is the instrumental power of smart technology (i.e, its predictive function, which for the sake of argument we will accept is more powerful than unassisted human prognostication) somehow a substitute for Law, as in its pragmatist conception?

In sections 7.3-7.4, Hildbrandt discusses the eponymous ends of law. These are not its functions as could be externally and sociologically validated, but rather its internally recognized goals or purposes. And these are not particular goals, such as environmental justice, that we might want particular laws to achieve. Rather, these are abstract goals that the law as an entire ‘regime of veridiction’ aims for. (“Veridiction” means “A statement that is true according to the worldview of a particular subject, rather than objectively true.” The idea is that the law has a coherent worldview of its own.

Hildebrandt’s description of law is robust and interesting. Law “articulates legal conditions for legal effect.” Legal personhood (a condition) entails certain rights under the law (an effect). These causes-and-effects are articulated in language, and this language does real work. In Austin’s terminology, legal language is performative–it performs things at an institutional and social level. Relatedly, the law is experienced as a lifeworld, or Welt, but not a monolithic lifeworld that encompasses all experience, but one of many worlds that we use to navigate reality, a ‘mode of existence’ that ‘affords specific roles, actors and actions while constraining others’. [She uses Latour to make this point, which in my opinion does not help.] It is interesting to compare this view of society with Nissenbaum’s ((2009) view of society differentiated into spheres, constituted by actor roles and norms.

In section 7.3.2, Hildebrandt draws on Gustav Radbruch for his theory of law. Consistent with her preceding arguments, she emphasizes that for Radbruch, law is antinomian, (a strange term) meaning that it is internally contradictory and unruly, with respect to its aims. And there are three such aims that are in tension:

  • Justice. Here, justice is used rather narrowly to mean that equal cases should be treated equally. In other words, the law must be applied justly/fairly across cases. To use her earlier framing, justice/equality implied that legal conditions cause legal effects in a consistent way. In my gloss, I would say this is equivalent to the formality of law, in the sense that the condition-effect rules must address the form of a case, and not treat particular cases differently. More substantively, Hildebrandt argues that Justice breaks down into more specific values: distributive justice, concerning the fair distribution of resources across society, and corrective justice, concerning the righting of wrongs through, e.g., torts.
  • Legal certainty. Legal rules must be binding and consistent, whether or not they achieve justice or purpose. “The certainty of the law requires its positivity; if it cannot be determined what is just, it must be decided what is lawful, and this from a position that is capable of enforcing the decision.” (Radbruch). Certainty about how the law will be applied, whether or not the application of the law is just (which may well be debated), is a good in itself. [A good example of this is law in business, which is famously one of the conditions for the rise of capitalism.]
  • Purpose. Beyond just/equal application of the law across cases and its predictable positivity, the law aims at other purposes such as social welfare, redistribution of income, guarding individual and public security, and so on. None of these purposes is inherent in the law, for Radbruch; but in his conception of law, by its nature it is directed by democratically determined purposes and is instrumental to them. These purposes may flesh out the normative detail that’s missing in a more abstract view of law.

Two moves by Hildebrandt in this section seem particularly substantial to her broader argument and corpus of work.

The first is the emphasis on the contrast between the antinomian conflict between justice, certainty, and purpose with the principle of legal certainty itself. Law, at any particular point in time, may fall short of justice or purpose, and must nevertheless be predictably applied. It also needs to be able to evolve towards its higher ends. This, for Hildebrandt, reinforces the essential ambiguous and linguistic character of law.

[Radbruch] makes it clear that a law that is only focused on legal certainty could not qualify as law. Neither can we expect the law to achieve legal certainty to the full, precisely because it must attend to justice and to purpose. If the attribution of legal effect could be automated, for instance by using a computer program capable of calculating all the relevant circumstances, legal certainty might be achieved. But this can only be done by eliminating the ambiguity that inheres in human language: it would reduce interpretation to mindless application. From Radbruch’s point of view this would fly in the face of the cultural, value-laden mode of existence of the law. It would refute the performative nature of law as an artificial construction that depends on the reiterant attribution of meaning and decision-making by mindful agents.

Hildebrandt, Smart Technologies, p. 149

The other move that seems particular to Hildebrandt is the connection she draws between purpose as one of the three primary ends of law and purpose-binding a feature of governance. The latter has particular relevance to technology law through its use in data protection, such as in the GDPR (which she addresses elsewhere in work like Hildebrandt, 2014). The idea here is that purposes do not just imply a positive direction of action; they also restrict activity to only those actions that support the purpose. This allows for separate institutions to exist in tension with each other and with a balance of power that’s necessary to support diverse and complex functions. Hildebrandt uses a very nice classical mythology reference here

The wisdom of the principle of purpose binding relates to Odysseus’s encounter with the Sirens. As the story goes, the Sirens lured passing sailors with the enchantment of their seductive voices, causing their ships to crash on the rocky coast. Odysseus wished to hear their song without causing a shipwreck; he wanted to have his cake and eat it too. While he has himself tied to the mast, his men have their ears plugged with beeswax. They are ordered to keep him tied tight, and to refuse any orders he gives to the contrary, while being under the spell of the Sirens as they pass their island. And indeed, though he is lured and would have caused death and destruction if his men had not been so instructed, the ship sails on. This is called self-binding. But it is more than that. There is a division of tasks that prevents him from untying himself. He is forced by others to live by his own rules. This is what purpose binding does for a constitutional democracy.

Hildebrandt, Smart Technologies, p. 156

I think what’s going on here is that Hildebrandt understands that actually getting the GDPR enforced over the whole digital environment is going to require a huge extension of the powers of law over business, organization, and individual practice. From some corners, there’s pessimism about the viability of the European data protection approach (Koops, 2014), arguing that it can’t really be understood or implemented well. Hildebrandt is making a big bet here, essentially saying: purpose-binding on data use is just a natural part of the power of law in general, as a socially performed practice. There’s nothing contingent about purpose-binding in the GDPR; it’s just the most recent manifestation of purpose as an end of law.

Commentary

It’s pretty clear what the agenda of this work is. Hildebrandt is defending the Rule of Law as a social practice of lawyers using admittedly ambiguous natural language over the ‘smart technologies’ that threaten it. This involves both a defense of law as being intrinsically about lawyers using ambiguous natural language, and the power of that law over businesses, etc. For the former, Hildebrandt invokes Radbruch’s view that law is antinomian. For the second point, she connects purpose-binding to purpose as an end of law.

I will continue to play the skeptic here. As is suggested in the quoted package, if one takes legal certainty seriously, then one could easily argue that software code leads to more certain outcomes than natural language based rulings. Moreover, to the extent that justice is a matter of legal formality–attention to the form of cases, and excluding from consideration irrelevant content–then that too weighs in favor of articulation of law in formal logic, which is relatively easy to translate into computer code.

Hildebrandt seems to think that there is something immutable about computer code, in a way that natural language is not. That’s wrong. Software is not built like bridges; software today is written by teams working rapidly to adapt it to many demands (Gürses and Hoboken, 2017). Recognizing this removes one of the major planks of Hildebrandt’s objection to computational law.

It could be argued that “legal certainty” implies a form of algorithmic interpretability: the key question is “certain for whom”. An algorithm that is opaque due to its operational complexity (Burrell, 2016) could, as an implementation of a legal decision, be less predictable to non-specialists than a simpler algorithm. So the tension in a lot of ‘algorithmic accountability’ literature between performance and interpretability would then play directly into the tension, within law, between purpose/instrumentality and certainty-to-citizens.

Overall, the argument here is not compelling yet as a refutation of the idea of law implemented as software code.

As for purpose-binding and the law, I think this may well be the true crux. I wonder if Hildebrandt develops it later in the book. There are not a lot of good computer science models of purpose binding. Tschantz, Datta, and Wing (2012) do a great job mapping out the problem but that research program has not resulted in robust technology for implementation. There may be deep philosophical/mathematical reasons why that is so. This is an angle I’ll be looking out for in further reading.

References

Burrell, Jenna. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data & Society3.1 (2016): 2053951715622512.

Gürses, Seda, and Joris Van Hoboken. “Privacy after the agile turn.” The Cambridge Handbook of Consumer Privacy. Cambridge Univ. Press, 2017. 1-29.

Hildebrandt, Mireille. “Location Data, Purpose Binding and Contextual Integrity: What’s the Message?.” Protection of Information and the Right to Privacy-A New Equilibrium?. Springer, Cham, 2014. 31-62.

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

Koops, Bert-Jaap. “The trouble with European data protection law.” International Data Privacy Law 4.4 (2014): 250-261.

Nissenbaum, Helen. Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press, 2009.

Tschantz, Michael Carl, Anupam Datta, and Jeannette M. Wing. “Formalizing and enforcing purpose restrictions in privacy policies.” 2012 IEEE Symposium on Security and Privacy. IEEE, 2012.

Beginning to read “Smart Technologies and the End(s) of Law” (Notes on: Hildebrandt, Smart Technologies, Sections 7.1-7.2)

I’m starting to read Mireille Hildebrandt‘s Smart Technologies and the End(s) of Law (2015) at the recommendation of several friends with shared interests in privacy and the tensions between artificial intelligence and the law. As has been my habit with other substantive books, I intend to blog my notes from reading as I get to it, in sections, in a perhaps too stream-of-consciousness, opinionated, and personally inflected way.

For reasons I will get to later, Hildebrandt’s book is a must-read for me. I’ve decided to start by jumping in on Chapter 7, because (a) I’m familiar enough with technology ethics, AI, and privacy scholarship to think I can skip that and come back as needed, and (b) I’m mainly reading because I’m interested in what a scholar of Hildebrandt’s stature says when she tackles the tricky problem of law’s response to AI head on.

I expect to disagree with Hildebrant in the end. We occupy different social positions and, as I’ve argued before, people’s position on various issues of technology policy appears to have a great deal to do with their social position or habitus. However, I know I have a good deal to learn about legal theory while having enough background in philosophy and social theory to parse through what Hildebrandt has to offer. And based on what I’ve read so far, I expect the contours of the possible positions that she draws out to be totally groundbreaking.

Notes on: Hildebrandt, Smart Technologies, §7.1-7.2

“The third part of this book inquires into the implications of smart technologies and data-driven agency for the law.”

– Hildebrandt, Smart Technologies,p.133

Lots of people write about how artificial intelligence presents an existential threat. Normally, they are talking about how a superintelligence is posing an existential threat to humanity. Hildebrandt is arguing something else: she is arguing that smart technologies may pose an existential threat to the law, or the Rule of Law. That is because the law’s “mode of existence” depends on written text, which is a different technical modality, with different affordances, than smart technology.

My take is that the mode of existence of modern law is deeply dependent upon the printing press and the way it has shaped our world. Especially the binary character of legal rules, the complexity of the legal system and the finality of legal decisions are affordances of — amongst things — the ICI [information and communication infrastructure] of the printing press.

– Hildebrandt, Smart Technologies, p.133

This is just so on point, it’s hard to know what to say. I mean, this is obviously on to something. But what?

To make her argument, Hildebrandt provides a crash course in philosophy of law and legal theory, distinguishing a number of perspectives that braid together into an argument. She discusses several different positions:

  • 7.2.1 Law as an essentially contested concept (Gallie). The concept of “law” [1] denotes something valuable, [2] covers intricate complexities, that makes it [3] inherently ambiguous and [4] necessarily vague. This [5] leads interested parties into contest over conceptions. The contest is [6] anchored in past, agreed upon exemplars of the concept, and [7] the contest itself sustains and develops the concept going forward. This is the seven-point framework of an “essentially contested concept”.
  • 7.2.2 Formal legal positivism. Law as a set of legal rules dictated by a sovereign (as opposed to law as a natural moral order) (Austin). Law as a coherent set of rules, defined by its unity (Kelsen). A distinction between substantive rules and rules about rule-making (Hart).
  • 7.2.3 Hermeneutic conceptions. The practice of law is about the creative interpretation of (e.g.) texts (case law, statutes, etc.) to application of new cases. The integrity of law (Dworkin) constrains this interpretation, but the projection of legal meaning into the future is part of the activity of legal practice. Judges “do things with words”–make performative utterances through their actions. Law is not just a system of rules, but a system of meaningful activity.
  • 7.2.3 Pragmatist conceptions (Realism legal positivism). As opposed to the formal legal positivism discusses earlier that sees law as rules, realist legal positivism sees law as a sociological phenomenon. Law is “prophecies of what the courts will do in fact, and nothing more pretentious” (Holmes). Pragmatism, as an epistemology, argues that the meaning of something is its practical effect; this approach could be seen as a constrained version of the hermeneutic concept of law.

To summarize Hildebrandt’s gloss on this material so far: Gallie’s “essentially contested concept” theory is doing the work of setting the stage for Hildebrant’s self-aware intervention into the legal debate. Hildebrandt is going to propose a specific concept of the law, and of the Rule of Law. She is doing this well-aware that this act of scholarship is engaging in contest.

Punchline

I detect in Hildebrandt’s writing a sympathy or preference for hermeneutic approaches to law. Indeed, by opening with Gallie, she sets up the contest about the concept of law as something internal to the hermeneutic processes of the law. These processes, and this contest, are about texts; the proliferation of texts is due to the role of the printing press in modern law. There is a coherent “integrity” to this concept of law.

The most interesting discussion, in my view, is loaded in to what reads like an afterthought: the pragmatist conception of law. Indeed, even at the level of formatting, pragmatism is buried: hermeneutic and pragmatist conceptions of law are combined into one section (7.2.3), where as Gallie and the formal positivists each get their own section (7.2.1 and 7.2.2).

This is odd, because the resonances between pragmatism and ‘smart technology’ are, in Hildebrandt’s admission, quite deep:

Basically, Holmes argued that law is, in fact, what we expect it to be, because it is this expectation that regulates our actions. Such expectations are grounded in past decisions, but if these were entirely deterministic of future decisions we would not need the law — we could settle for logic and simply calculate the outcome of future decisions. No need for interpretation. Holmes claimed, however, that ‘the life of law has not been logic. It has been experience.’ This correlates with a specific conception of intelligence. As we have seen in Chapter 2 and 3, rule-based artificial intelligence, which tried to solve problems by means of deductive logic, has been superseded by machine learning (ML), based on experience.

– Hildebrandt, Smart Technologies, p.142

Hildebrandt considers this connection between pragmatist legal interpretation and machine learning only to reject it summarily in a single paragraph at the end of the section.

If we translate [a maxim of classical pragmatist epistemology] into statistical forecasts we arrive at judgments resulting from ML. However, neither logic nor statistics can attribute meaning. ML-based court decisions would remove the fundamental ambiguity of human language from the centre stage of the law. As noted above, this ambiguity is connected with the value-laden aspect of the concept of law. It is not a drawback of natural language, but what saves us from acting like mindless agents. My take is that an approach based on statistics would reduce judicial and legislative decisions to administration, and thus collapse the Rule of Law. This is not to say that a number of administrative decisions could not be taken by smart computing systems. It is to confirm that such decisions should be brought under the Rule of Law, notably by making them contestable in a court of law.

– Hildebrandt, Smart Technologies, p.143

This is a clear articulation of Hildebrandt’s agenda (“My take is that…”). It is also clearly an aligning the practice of law with contest, ambiguity, and interpretation as opposed to “mindless” activity. Natural language’s ambiguity is a feature, not a bug. Narrow pragmatism, which is aligned with machine learning, is a threat to the Rule of Law

Some reflections

Before diving into the argument, I have to write a bit about my urgent interest in the book. Though I only heard about it recently, my interests have tracked the subject matter for some time.

For some time I have been interested in the connection between philosophical pragmatism and the concerns about AI, which I believe can be traced back to Horkheimer. But I thought nobody was giving the positive case for pragmatism its due. At the end of 2015, totally unaware of “Smart Technologies” (my professors didn’t seem aware of it either…), I decided that I would write my doctoral dissertation thesis defending the bold thesis that yes, we should have AI replace the government. A constitution written in source code. I was going to back the argument up with, among other things, pragmatist legal theory.

I had to drop the argument because I could not find faculty willing to be on the committee for such a dissertation! I have been convinced ever since that this is a line of argument that is actually rather suppressed. I was able to articulate the perspective in a philosophy journal in 2016, but had to abandon the topic.

This was probably good in the long run, since it meant I wrote a dissertation on privacy which addressed many of the themes I was interested in, but in greater depth. In particular, working with Helen Nissenbaum I learned about Hildebrandt’s articles comparing contextual integrity with purpose binding in the GDPR (Hildebrandt, 2013; Hildebrandt, 2014), which at the time my mentors at Berkeley seemed unaware of. I am still working on puzzles having to do with algorithmic implementation or response to the law, and likely will for some time.

Recently, been working at a Law School and have reengaged the interdisciplinary research community at venues like FAT*. This has led me, seemingly unavoidably, back to what I believe to be the crux of disciplinary tension today: the rising epistemic dominance of pragmatist computational statistics–“data science”and its threat to humanistic legal authority, which is manifested in the clash of institutions that are based on each, e.g., iconically, “Silicon Valley” (or Seattle) and the European Union. Because of the explicitly normative aspects of humanistic legal authority, it asserts itself again and again as an “ethical” alternative to pragmatist technocratic power. This is the latest manifestation of a very old debate.

Hildebrandt is the first respectable scholar (a category from which I exclude myself) that I’ve encountered to articulate this point. I have to see where she takes the argument.

So far, however, I think here argument begs the question. Implicitly, the “essentially contested” character of law is due to the ambiguity of natural language and the way in which that necessitates contest over the meaning of words. And so we have a professional class of lawyers and scholars that debate the meaning of words. I believe the the regulatory power of this class is what Hildebrandt refers to as “the Rule of Law”.

While it’s true that an alternative regulatory mechanism based on statistical prediction would be quite different from this sense of “Rule of Law”, it is not clear from Hildebrandt’s argument, yet, why her version of “Rule of Law” is better. The only hint of an argument is the problem of “mindless agents”. Is she worried about the deskilling of the legal profession, or the reduced need for elite contest over meaning? What is hermeneutics offering society, outside of the bounds of its own discourse?

References

Benthall, S. (2016). Philosophy of computational social science. Cosmos and History: The Journal of Natural and Social Philosophy12(2), 13-30.

Sebastian Benthall. Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics. Ph.D. dissertation. Advisors: John Chuang and Deirdre Mulligan. University of California, Berkeley. 2018.

Hildebrandt, Mireille. “Slaves to big data. Or are we?.” (2013).

Hildebrandt, Mireille. “Location Data, Purpose Binding and Contextual Integrity: What’s the Message?.” Protection of Information and the Right to Privacy-A New Equilibrium?. Springer, Cham, 2014. 31-62.

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