ethical data science is statistical data science #dsesummit

I am at the Moore/Sloan Data Science Environment at the Suncadia Resort in Washington. There are amazing trees here. Wow!

So far the coolest thing I’ve seen is a talk on how Dynamic Mode Decomposition, a technique from fluid dynamics, is being applied to data from brains.

And yet, despite all this sweet science, all is not well in paradise. Provocations, source unknown, sting the sensitive hearts of the data scientists here. Something or someone stirs our emotional fluids.

There are two controversies. There is one solution, which is the synthesis of the two parts into a whole.

Herr Doctor Otherwise Anonymous confronted some compatriots and myself in the resort bar with a distressing thought. His work in computational analysis of physical materials–his data science–might be coopted and used for mass surveillance. Powerful businesses might use the tools he creates. Information discovered through these tools may be used to discriminate unfairly against the underprivileged. As teachers, responsible for the future through our students, are we not also responsible for teaching ethics? Should we not be concerned as practitioners; should we not hesitate?

I don’t mind saying that at the time I at my Ballmer Peak of lucidity. Yes, I replied, we should teach our students ethics. But we should not base our ethics in fear! And we should have the humility to see that the moral responsibility is not ours to bear alone. Our role is to develop good tools. Others may use them for good or ill, based on their confidence in our guarantees. Indeed, an ethical choice is only possible when one knows enough to make sound judgment. Only when we know all the variables in play and how they relate to each other can we be sure our moral decisions–perhaps to work for social equality–are valid.

Later, I discover that there is more trouble. The trouble is statistics. There is a matter of professional identity: Who are statisticians? Who are data scientists? Are there enough statisticians in data science? Are the statisticians snubbing the data scientists? Do they think they are holier-than-thou? Are the data scientists merely bad scientists, slinging irresponsible model-fitting code, inviting disaster?


Attachment to personal identity is the root of all suffering. Put aside all sociological questions of who gets to be called a statistician for a moment. Don’t even think about what branches of mathematics are considered part of a core statistical curriculum. These are historical contingencies with no place in the Absolute.

At the root of this anxiety about what is holy, and what is good science, is that statistical rigor just is the ethics of data science.

Ethnography, philosophy, and data anonymization

The other day at BIDS I was working at my laptop when a rather wizardly looking man in a bicycle helmet asked me when The Hacker Within would be meeting. I recognized him from a chance conversation in an elevator after Anca Dragan’s ICBS talk the previous week. We had in that brief moment connected over the fact that none of the bearded men in the elevator had remembered to press the button for the ground floor. We had all been staring off into space before a young programmer with a thin mustache pointed out our error.

Engaging this amicable fellow, whom I will leave anonymous, the conversation turned naturally towards principles for life. I forget how we got onto the topic, but what I took away from the conversation was his advice: “Don’t turn your passion into your job. That’s like turning your lover into a whore.”

Scholars in the School of Information are sometimes disparaging of the Data-Information-Knowledge-Wisdom hierarchy. Scholars, I’ve discovered, are frequently disparaging of ideas that are useful, intuitive, and pertinent to action. One cannot continue to play the Glass Bead Game if it has already been won and more than one can continue to be entertained by Tic Tac Toe once one has grasped its ineluctable logic.

We might wonder, as did Horkheimer, when the search and love of wisdom ceased to be the purpose of education. It may have come during the turn when philosophy was determined to be irrelevant, speculative or ungrounded. This perhaps coincided, in the United States, with McCarthyism. This is a question for the historians.

What is clear now is that philosophy per se is not longer considered relevant to scientific inquiry.

An ethnographer I know (who I will leave anonymous) told me the other day that the goal of Science and Technology Studies is to answer questions from philosophy of science with empirical observation. An admirable motivation for this is that philosophy of science should be grounded in the true practice of science, not in idle speculation about it. The ethnographic methods, through which observational social data is collected and then compellingly articulated, provide a kind of persuasiveness that for many far surpasses the persuasiveness of a priori logical argument, let alone authority.

And yet the authority of ethnographic writing depends always on the socially constructed role of the ethnographer, much like the authority of the physicist depends on their socially constructed role as physicists. I’d even argue that the dependence of ethnographic authority on social construction is greater than that of other kinds of scientific authority, as ethnography is so quintessentially an embedded social practice. A physicist or chemist or biologist at least in principle has nature to push back on their claims; a renegade natural scientist can as a last resort claim their authority through provision of a bomb or a cure. The mathematician or software engineer can test and verify their work through procedure. The ethnographer does not have these opportunity. Their writing will never be enough to convey the entirety of their experience. It is always partial evidence, a gesture at the unwritten.

This is not an accidental part of the ethnographic method. The practice of data anonymization, necessitated by the IRB and ethics, puts limitations on what can be said. These limitations are essential for building and maintaining the relationships of trust on which ethnographic data collection depends. The experiences of the ethnographer must always go far beyond what has been regulated as valid procedure. The information they have collected illicitly will, if they are skilled and wise, inform their judgment of what to write and what to leave out. The ethnographic text contains many layers of subtext that will be unknown to most readers. This is by design.

The philosophical text, in contrast, contains even less observational data. The text is abstracted from context. Only the logic is explicit. A naive reader will assume, then, that philosophy is a practice of logic chopping.

This is incorrect. My friend the ethnographer was correct: that ethnography is a way of answering philosophical questions empirically, through experience. However, what he missed is that philosophy is also a way of answering philosophical questions through experience. Just as in ethnographic writing, experience necessarily shapes the philosophical text. What is included, what is left out, what constellation in the cosmos of ideas is traced by the logic of the argument–these will be informed by experience, even if that experience is absent from the text itself.

One wonders: thus unhinged from empirical argument, how does a philosophical text become authoritative?

I’d offer the answer: it doesn’t. A philosophical text does not claim authority. That has been its method since Socrates.

de Beauvoir on science as human freedom

I appear to be unable to stop writing blog posts about philosophers who wrote in the 1940’s. I’ve been attempting a kind of survey. After a lot of reading, I have to say that my favorite–the one I think is most correct–is Simone de Beauvoir.

Much like “bourgeois”, “de Beauvoir” is something I find it impossible to remember how to spell. Therefore I am setting myself up for embarrassment by beginning to write about her work, The Ethics of Ambiguity. On the other hand, it’s nice to come full circle. In a notebook I was scribbling in when I first showed up in graduate school I was enthusiastic about using de Beauvoir to explicate what’s interesting about open source software development. Perhaps now is the right time to indulge the impulse.

de Beauvoir is generally not considered to be a philosopher of science. That’s too bad, because she said some of the most brilliant things about science ever said. If you can get past just a little bit of existentialist jargon, there’s a lot there.

Here’s a passage. The Marxists have put this entire book on the Internet, making it easy to read.

To will freedom and to will to disclose being are one and the same choice; hence, freedom takes a positive and constructive step which causes being to pass to existence in a movement which is constantly surpassed. Science, technics, art, and philosophy are indefinite conquests of existence over being; it is by assuming themselves as such that they take on their genuine aspect; it is in the light of this assumption that the word progress finds its veridical meaning. It is not a matter of approaching a fixed limit: absolute Knowledge or the happiness of man or the perfection of beauty; all human effort would then be doomed to failure, for with each step forward the horizon recedes a step; for man it is a matter of pursuing the expansion of his existence and of retrieving this very effort as an absolute.

de Beauvoir’s project in The Ethics of Ambiguity is to take seriously the antimonies of society and the individual, of nature and the subject, which Horkheimer only gets around to stating at the conclusion of contemporary analysis. Rather than cry from wounds of getting skewered by the horns of the antinomy, de Beauvoir turns that ambiguity inherent in the antinomy into a realistic, situated ethics.

If de Beauvoir’s ethics have a telos or purpose, it is to expand human freedom and potential indefinitely. Through a terrific dialectical argument, she reasons out why this project is in a sense the only honest one for somebody in the human condition, despite its transcendence over individual interest.

Science, then, becomes one of several activities which one undertakes to expand this human potential.

Science condemns itself to failure when, yielding to the infatuation of the serious, it aspires to attain being, to contain it, and to possess it; but it finds its truth if it considers itself as a free engagement of thought in the given, aiming, at each discovery, not at fusion with the thing, but at the possibility of new discoveries; what the mind then projects is the concrete accomplishment of its freedom.

Science is the process of free inquiry, not the product of a particular discovery. The finest scientific discoveries open up new discoveries.

What about technics?

The attempt is sometimes made to find an objective justification of science in technics; but ordinarily the mathematician is concerned with mathematics and the physicist with physics, and not with their applications. And, furthermore, technics itself is not objectively justified; if it sets up as absolute goals the saving of time and work which it enables us to realize and the comfort and luxury which it enables us to have access to, then it appears useless and absurd, for the time that one gains can not be accumulated in a store house; it is contradictory to want to save up existence, which, the fact is, exists only by being spent, and there is a good case for showing that airplanes, machines, the telephone, and the radio do not make men of today happier than those of former times.

Here we have in just a couple sentences dismissal of instrumentality as the basis for science. Science is not primarily for acceleration; this is absurd.

But actually it is not a question of giving men time and happiness, it is not a question of stopping the movement of life: it is a question of fulfilling it. If technics is attempting to make up for this lack, which is at the very heart of existence, it fails radically; but it escapes all criticism if one admits that, through it, existence, far from wishing to repose in the security of being, thrusts itself ahead of itself in order to thrust itself still farther ahead, that it aims at an indefinite disclosure of being by the transformation of the thing into an instrument and at the opening of ever new possibilities for man.

For de Beauvoir, science (as well as all the other “constructive activities of man” including art, etc.) should be about the disclosure of new possibilities.

Succinct and unarguable.

scientific contexts


  • For Helen Nissenbaum (contextual integrity theory):
    • a context is a social domain that is best characterized by its purpose. For example, a hospital’s purpose is to cure the sick and wounded.
    • a context also has certain historically given norms of information flow.
    • a violation of a norm of information flow in a given context is a potentially unethical privacy violation. This is an essentially conservative notion of privacy, which is balanced by the following consideration…
    • Whether or not a norm of information flow should change (given, say, a new technological affordance to do things in a very different way) can be evaluated by how well it serve the context’s purpose.
  • For Fred Dretske (Knowledge and the Flow of Information, 1983):
    • The appropriate definition of information is (roughly) just what it takes to know something. (More specifically: M carries information about X if it reliably transmits what it takes for a suitably equipped but otherwise ignorant observer to learn about X.)
  • Combining Nissenbaum and Dretske, we see that with an epistemic and naturalized understanding of information, contextual norms of information flow are inclusive of epistemic norms.
  • Consider scientific contexts. I want to use ‘science’ in the broadest possible (though archaic) sense of the intellectual and practical activity of study or coming to knowledge of any kind. “Science” from the Latin “scire”–to know. Or “Science” (capitalized) as the translated 19th Century German Wissenschaft.
    • A scientific context is one whose purpose is knowledge.
    • Specific issues of whose knowledge, knowledge about what, and to what end the knowledge is used will vary depending on the context.
    • As information flow is necessary for knowledge, the purpose of science, the norms of information flow within (and without) a scientific context, the integrity of scientific context will be especially sensitive to its norms of information flow.
  • An insight I owe to my colleague Michael Tschantz, in conversation, is that there are several open problems within contextual integrity theory:
    • How does one know what context one is in? Who decides that?
    • What happens at the boundary between contexts, for example when one context is embedded in another?
    • Are there ways for the purpose of a context to change (not just the norms within it)?
  • Proposal: One way of discovering what a science is is to trace what its norms of information flow and to identify its purpose. A contrast between the norms and purpose of, for example, data science and ethnography, would be illustrative of both. One approach to this problem could be kind of qualitative research done by Edwin Hutchins on distributed cognition, which accepts a naturalized view of information (necessary for this framing) and then discovers information flows in a context through qualitative observation.

The recalcitrance of prediction

We have identified how Bostrom’s core argument for superintelligence explosion depends on a crucial assumption. An intelligence explosion will happen only if the kinds of cognitive capacities involved in instrumental reason are not recalcitrant to recursive self-improvement. If recalcitrance rises comparably with the system’s ability to improve itself, then the takeoff will not be fast. This significantly decreases the probability of decisively strategic singleton outcomes.

In this section I will consider the recalcitrance of intelligent prediction, which is one of the capacities that is involved in instrumental reason (another being planning). Prediction is a very well-studied problem in artificial intelligence and statistics and so is easy to characterize and evaluate formally.

Recalcitrance is difficult to formalize. Recall that in Bostrom’s formulation:

\frac{dI}{dt} = \frac{O(I)}{R(I)}

One difficulty in analyzing this formula is that the units are not specified precisely. What is a “unit” of intelligence? What kind of “effort” is the unit of optimization power? And how could one measure recalcitrance?

A benefit of looking at a particular intelligent task is that it allows us to think more concretely about what these terms mean. If we can specify which tasks are important to consider, then we can take the level of performance on those well-specified class of problems as measures of intelligence.

Prediction is one such problem. In a nutshell, prediction comes down to estimating a probability distribution over hypotheses. Using the Bayesian formulation of statistical influence, we can represent the problem as:

P(H|D) = \frac{P(D|H) P(H)}{P(D)}

Here, P(H|D) is the posterior probability of a hypothesis H given observed data D. If one is following statistically optimal procedure, one can compute this value by taking the prior probability of the hypothesis P(H), multiplying it by the likelihood of the data given the hypothesis P(D|H), and then normalizing this result by dividing by the probability of the data over all models, P(D) = \sum_{i}P(D|H_i)P(H_i).

Statisticians will justifiably argue whether this is the best formulation of prediction. And depending on the specifics of the task, the target value may well be some function of posterior (such as the hypothesis with maximum likelihood) and the overall distribution may be secondary. These are valid objections that I would like to put to one side in order to get across the intuition of an argument.

What I want to point out is that if we look at the factors that affect performance on prediction problems, there a very few that could be subject to algorithmic self-improvement. If we think that part of what it means for an intelligent system to get more intelligent is to improve its ability of prediction (which Bostrom appears to believe), but improving predictive ability is not something that a system can do via self-modification, then that implies that the recalcitrance of prediction, far from being constant or lower, actually approaches infinity with respect the an autonomous system’s capacity for algorithmic self-improvement.

So, given the formula above, in what ways can an intelligent system improve its capacity to predict? We can enumerate them:

  • Computational accuracy. An intelligent system could be better or worse at computing the posterior probabilities. Since most of the algorithms that do this kind of computation do so with numerical approximation, there is the possibility of an intelligent system finding ways to improve the accuracy of this calculation.
  • Computational speed. There are faster and slower ways to compute the inference formula. An intelligent system could come up with a way to make itself compute the answer faster.
  • Better data. The success of inference is clearly dependent on what kind of data the system has access to. Note that “better data” is not necessarily the same as “more data”. If the data that the system learns from is from a biased sample of the phenomenon in question, then a successful Bayesian update could make its predictions worse, not better. Better data is data that is informative with respect to the true process that generated the data.
  • Better prior. The success of inference depends crucially on the prior probability assigned to hypotheses or models. A prior is better when it assigns higher probability to the true process that generates observable data, or models that are ‘close’ to that true process. An important point is that priors can be bad in more than one way. The bias/variance tradeoff is well-studied way of discussing this. Choosing a prior in machine learning involves a tradeoff between:
    1. Bias. The assignment of probability to models that skew away from the true distribution. An example of a biased prior would be one that gives positive probability to only linear models, when the true phenomenon is quadratic. Biased priors lead to underfitting in inference.
    2. Variance.The assignment of probability to models that are more complex than are needed to reflect the true distribution. An example of a high-variance prior would be one that assigns high probability to cubic functions when the data was generated by a quadratic function. The problem with high variance priors is that they will overfit data by inferring from noise, which could be the result of measurement error or something else less significant than the true generative process.

    In short, there best prior is the correct prior, and any deviation from that increases error.

Now that we have enumerate the ways in which an intelligent system may improve its power of prediction, which is one of the things that’s necessary for instrumental reason, we can ask: how recalcitrant are these factors to recursive self-improvement? How much can an intelligent system, by virtue of its own intelligence, improve on any of these factors?

Let’s start with computational accuracy and speed. An intelligent system could, for example, use some previously collected data and try variations of its statistical inference algorithm, benchmark their performance, and then choose to use the most accurate and fastest ones at a future time. Perhaps the faster and more accurate the system is at prediction generally, the faster and more accurately it would be able to engage in this process of self-improvement.

Critically, however, there is a maximum amount of performance that one can get from improvements to computational accuracy if you hold the other factors constant. You can’t be more accurate than perfectly accurate. Therefore, at some point recalcitrance of computational accuracy rises to infinity. Moreover, we would expect that effort made at improving computational accuracy would exhibit diminishing returns. In other words, recalcitrance of computational accuracy climbs (probably close to exponentially) with performance.

What is the recalcitrance of computational speed at inference? Here, performance is limited primarily by the hardware on which the intelligent system is implemented. In Bostrom’s account of superintelligence explosion, he is ambiguous about whether and when hardware development counts as part of a system’s intelligence. What we can say with confidence, however, is that for any particular piece of hardware there will be a maximum computational speed attainable with with, and that recursive self-improvement to computational speed can at best approach and attain this maximum. At that maximum, further improvement is impossible and recalcitrance is again infinite.

What about getting better data?

Assuming an adequate prior and the computational speed and accuracy needed to process it, better data will always improve prediction. But it’s arguable whether acquiring better data is something that can be done by an intelligent system working to improve itself. Data collection isn’t something that the intelligent system can do autonomously, since it has to interact with the phenomenon of interest to get more data.

If we acknowledge that data collection is a critical part of what it takes for an intelligent system to become more intelligent, then that means we should shift some of our focus away from “artificial intelligence” per se and onto ways in which data flows through society and the world. Regulations about data locality may well have more impact on the arrival of “superintelligence” than research into machine learning algorithms now that we have very faster, very accurate algorithms already. I would argue that the recent rise in interest in artificial intelligence is due mainly to availability of vast amounts of new data through sensors and the Internet. Advances in computational accuracy and speed (such as Deep Learning) have to catch up to this new availability of data and use new hardware, but data is the rate limiting factor.

Lastly, we have to ask: can a system improve its own prior, if data, computational speed, and computational accuracy are constant?

I have to argue that it can’t do this in any systematic way, if we are looking at the performance of the system at the right level of abstraction. Potentially a machine learning algorithm could modify its prior if it sees itself as underperforming in some ways. But there is a sense in which any modification to the prior made by the system that is not a result of a Bayesian update is just part of the computational basis of the original prior. So recalcitrance of the prior is also infinite.

We have examined the problem of statistical inference and ways that an intelligent system could improve its performance on this task. We identified four potential factors on which it could improve: computational accuracy, computational speed, better data, and a better prior. We determined that contrary to the assumption of Bostrom’s hard takeoff argument, the recalcitrance of prediction is quite high, approaching infinity in the cases of computational accuracy, computational speed, and the prior. Only data collections to be flexibly recalcitrant. But data collection is not a feature of the intelligent system alone but also depends on its context.

As a result, we conclude that the recalcitrance of prediction is too high for an intelligence explosion that depends on it to be fast. We also note that those concerned about superintelligent outcomes should shift their attention to questions about data sourcing and storage policy.

Nissenbaum the functionalist

Today in Classics we discussed Helen Nissenbaum’s Privacy in Context.

Most striking to me is that Nissenbaum’s privacy framework, contextual integrity theory, depends critically on a functionalist sociological view. A context is defined by its information norms and violations of those norms are judged according to their (non)accordance with the purposes and values of the context. So, for example, the purposes of an educational institution determine what are appropriate information norms within it, and what departures from those norms constitute privacy violations.

I used to think teleology was dead in the sciences. But recently I learned that it is commonplace in biology and popular in ecology. Today I learned that what amounts to a State Philosopher in the U.S. (Nissenbaum’s framework has been more or less adopted by the FTC) maintains a teleological view of social institutions. Fascinating! Even more fascinating that this philosophy corresponds well enough to American law as to be informative of it.

From a “pure” philosophy perspective (which is I will admit simply a vice of mine), it’s interesting to contrast Nissenbaum with…oh, Horkheimer again. Nissenbaum sees ethical behavior (around privacy at least) as being behavior that is in accord with the purpose of ones context. Morality is given by the system. For Horkheimer, the problem is that the system’s purposes subsume the interests of the individual, who is alone the agent who is able to determine what is right and wrong. Horkheimer is a founder of a Frankfurt school, arguably the intellectual ancestor of progressivism. Nissenbaum grounds her work in Burke and her theory is admittedly conservative. Privacy is violated when people’s expectations of privacy are violated–this is coming from U.S. law–and that means people’s contextual expectations carry more weight than an individual’s free-minded beliefs.

The tension could be resolved when free individuals determine the purpose of the systems they participate in. Indeed, Nissenbaum quotes Burke in his approval of established conventions as being the result of accreted wisdom and rationale of past generations. The system is the way it is because it was chosen. (Or, perhaps, because it survived.)

Since Horkheimer’s objection to “the system” is that he believes instrumentality has run amok, thereby causing the system serve a purpose nobody intended for it, his view is not inconsistent with Nissenbaum’s. Nissenbaum, building on Dworkin, sees contextual legitimacy as depending on some kind of political legitimacy.

The crux of the problem is the question of what information norms comprise the context in which political legitimacy is formed, and what purpose does this context or system serve?

The relationship between Bostrom’s argument and AI X-Risk

One reason why I have been writing about Bostrom’s superintelligence argument is because I am acquainted with what could be called the AI X-Risk social movement. I think it is fair to say that this movement is a subset of Effective Altruism (EA), a laudable movement whose members attempt to maximize their marginal positive impact on the world.

The AI X-Risk subset, which is a vocal group within EA, sees the emergence of a superintelligent AI as one of several risks that is notably because it could ruin everything. AI is considered to be a “global catastrophic risk” unlike more mundane risks like tsunamis and bird flu. AI X-Risk researchers argue that because of the magnitude of the consequences of the risk they are trying to anticipate, they must raise more funding and recruit more researchers.

While I think this is noble, I think it is misguided for reasons that I have been outlining in this blog. I am motivated to make these arguments because I believe that there are urgent problems/risks that are conceptually adjacent (if you will) to the problem AI X-Risk researchers study, but that the focus on AI X-Risk in particular diverts interest away from them. In my estimation, as more funding has been put into evaluating potential risks from AI many more “mainstream” researchers have benefited and taken on projects with practical value. To some extent these researchers benefit from the alarmism of the AI X-Risk community. But I fear that their research trajectory is thereby distorted from where it could truly provide maximal marginal value.

My reason for targeting Bostrom’s argument for the existential threat of superintelligent AI is that I believe it’s the best defense of the AI X-Risk thesis out there. In particular, if valid the argument should significantly raise the expected probability of an existentially risky AI outcome. For Bostrom, it is likely a natural consequence of advancement in AI research more generally because of recursive self-improvement and convergent instrumental values.

As I’ve informally work shopped this argument I’ve come upon this objection: Even if it is true that a superintelligent system would not for systematic reasons become a existentially risky singleton, that does not mean that somebody couldn’t develop such a superintelligent system in an unsystematic way. There is still an existential risk, even if it is much lower. And because existential risks are so important, surely we should prepare ourselves for even this low probability event.

There is something inescapable about this logic. However, the argument applies equally well to all kinds of potential apocalypses, such as enormous meteors crashing into the earth and biowarfare produced zombies. Without some kind of accounting of the likelihood of these outcomes, it’s impossible to do a rational budgeting.

Moreover, I have to call into question the rationality of this counterargument. If Bostrom’s arguments are used in defense of the AI X-Risk position but then the argument is dismissed as unnecessary when it is challenged, that suggests that the AI X-Risk community is committed to their cause for reasons besides Bostrom’s argument. Perhaps these reasons are unarticulated. One could come up with all kinds of conspiratorial hypotheses about why a group of people would want to disingenuously spread the idea that superintelligent AI poses an existential threat to humanity.

The position I’m defending on this blog (until somebody convinces me otherwise–I welcome all comments) is that a superintelligent AI singleton is not a significantly likely X-Risk. Other outcomes that might be either very bad or very good, such as ones with many competing and cooperating superintelligences, are much more likely. I’d argue that it’s more or less what we have today, if you consider sociotechnical organizations as a form of collective superintelligence. This makes research into this topic not only impactful in the long run, but also relevant to problems faced by people now and in the near future.

Bostrom and Habermas: technical and political moralities, and the God’s eye view

An intriguing chapter that follows naturally from Nick Bostrom’s core argument is his discussion of machine ethics writ large. He asks: suppose one could install into an omnipotent machine ethical principles, trusting it with the future of humanity. What principles should we install?

What Bostrom accomplishes by positing his Superintelligence (which begins with something simply smarter than humans, and evolves over the course of the book into something that takes over the galaxy) is a return to what has been called “the God’s eye view”. Philosophers once attempted to define truth and morality according to perspective of an omnipotent–often both transcendent and immanent–god. Through the scope of his work, Bostrom has recovered some of these old themes. He does this not only through his discussion of Superintelligence (and positing its existence in other solar systems already) but also through his simulation arguments.

The way I see it, one thing I am doing by challenging the idea of an intelligence explosion and its resulting in a superintelligent singleton is problematizing this recovery of the God’s Eye view. If your future world is governed by many sovereign intelligent systems instead of just one, then ethics are something that have to emerge from political reality. There is something irreducibly difficult about interacting with other intelligences and it’s from this difficulty that we get values, not the other way around. This sort of thinking is much more like Habermas’s mature ethical philosophy.

I’ve written about how to apply Habermas to the design of networked publics that mediate political interactions between citizens. What I built and offer as toy example in that paper, @TheTweetserve, is simplistic but intended just as a proof of concept.

As I continue to read Bostrom, I expect a convergence on principles. “Coherent extrapolated volition” sounds a lot like a democratic governance structure with elected experts at first pass. The question of how to design a governance structure or institution that leverages artificial intelligence appropriately while legitimately serving its users motivates my dissertation research. My research so far has only scratched the surface of this problem.

Recalcitrance examined: an analysis of the potential for superintelligence explosion

To recap:

  • We have examined the core argument from Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies regarding the possibility of a decisively strategic superintelligent singleton–or, more glibly, an artificial intelligence that takes over the world.
  • With an eye to evaluating whether this outcome is particularly likely relative to other futurist outcomes, we have distilled the argument and in so doing have reduced it to a simpler problem.
  • That problem is to identify bounds on the recalcitrance of the capacities that are critical for instrumental reasoning. Recalcitrance is defined as the inverse of the rate of increase to intelligence per time per unit of effort put into increasing that intelligence. It is meant to capture how hard it is to make an intelligent system smarter, and in particular how hard it is for an intelligent system to make itself smarter. Bostrom’s argument is that if an intelligent system’s recalcitrance is constant or lower, then it is possible for the system to undergo an “intelligence explosion” and take over the world.
  • By analyzing how Bostrom’s argument depends only on the recalcitrance of instrumentality, and not of the recalcitrance of intelligence in general, we can get a firmer grip on the problem. In particular, we can focus on such tasks as prediction and planning. If we discover that these tasks are in fact significantly recalcitrant that should reduce our expected probability of an AI singleton and consequently cause us to divert research funds to problems that anticipate other outcomes.

In this section I will look in further depth at the parts of Bostrom’s intelligence explosion argument about optimization power and recalcitrance. How recalcitrant must a system be for it to not be susceptible to an intelligence explosion?

This section contains some formalism. For readers uncomfortable with that, trust me: if the system’s recalcitrance is roughly proportional to the amount that the system is able to invest in its own intelligence, then the system’s intelligence will not explode. Rather, it will climb linearly. If the system’s recalcitrance is significantly greater than the amount that the system can invest in its own intelligence, then the system’s intelligence won’t even climb steadily. Rather, it will plateau.

To see why, recall from our core argument and definitions that:

Rate of change in intelligence = Optimization power / Recalcitrance.

Optimization power is the amount of effort that is put into improving the intelligence of system. Recalcitrance is the resistance of that system to improvement. Bostrom presents this as a qualitative formula then expands it more formally in subsequent analysis.

\frac{dI}{dt} = \frac{O(I)}{R}

Bostrom’s claim is that for instrumental reasons an intelligent system is likely to invest some portion of its intelligence back into improving its intelligence. So, by assumption we can model O(I) = \alpha I + \beta for some parameters \alpha and \beta, where 0 < \alpha < 1 and \beta represents the contribution of optimization power by external forces (such as a team of researchers). If recalcitrance is constant, e.g R = k, then we can compute:

\Large \frac{dI}{dt} = \frac{\alpha I + \beta}{k}

Under these conditions, I will be exponentially increasing in time t. This is the “intelligence explosion” that gives Bostrom’s argument so much momentum. The explosion only gets worse if recalcitrance is below a constant.

In order to illustrate how quickly the “superintelligence takeoff” occurs under this model, I’ve plotted the above function plugging in a number of values for the parameters \alpha, \beta and k. Keep in mind that the y-axis is plotted on a log scale, which means that a roughly linear increase indicates exponential growth.

Plot of exponential takeoff rates

Modeled superintelligence takeoff where rate of intelligence gain is linear in current intelligence and recalcitrance is constant. Slope in the log scale is determine by alpha and k values.

It is true that in all the above cases, the intelligence function is exponentially increasing over time. The astute reader will notice that by my earlier claim \alpha cannot be greater than 1, and so one of the modeled functions is invalid. It’s a good point, but one that doesn’t matter. We are fundamentally just modeling intelligence expansion as something that is linear on the log scale here.

However, it’s important to remember that recalcitrance may also be a function of intelligence. Bostrom does not mention the possibility of recalcitrance being increasing in intelligence. How sensitive to intelligence would recalcitrance need to be in order to prevent exponential growth in intelligence?

Consider the following model where recalcitrance is, like optimization power, linearly increasing in intelligence.

\frac{dI}{dt} = \frac{\alpha_o I + \beta_o}{\alpha_r I + \beta_r}

Now there are four parameters instead of three. Note this model is identical to the one above it when \alpha_r = 0. Plugging in several values for these parameters and plotting again with the y-scale on the log axis, we get:

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

The point of this plot is to illustrate how easily exponential superintelligence takeoff might be stymied by a dependence of recalcitrance on intelligence. Even in the absurd case where the system is able to invest a thousand times as much intelligence that it already has back into its own advancement, and a large team steadily commits a million “units” of optimization power (whatever that means–Bostrom is never particularly clear on the definition of this), a minute linear dependence of recalcitrance on optimization power limits the takeoff to linear speed.

Are the reasons to think that recalcitrance might increase as intelligence increases? Prima facie, yes. Here’s a simple thought experiment: What if there is some distribution of intelligence algorithm advances that are available in nature and that some of them are harder to achieve than others. A system that dedicates itself to advancing its own intelligence, knowing that it gets more optimization power as it gets more intelligent, might start by finding the “low hanging fruit” of cognitive enhancement. But as it picks the low hanging fruit, it is left with only the harder discoveries. Therefore, recalcitrance increases as the system grows more intelligent.

This is not a decisive argument against fast superintelligence takeoff and the possibility of a decisively strategic superintelligent singleton. Above is just an argument about why it is important to consider recalcitrance carefully when making claims about takeoff speed, and to counter what I believe is a bias in Bostrom’s work towards considering unrealistically low recalcitrance levels.

In future work, I will analyze the kinds of instrumental intelligence tasks, like prediction and planning, that we have identified as being at the core of Bostrom’s superintelligence argument. The question we need to ask is: does the recalcitrance of prediction tasks increase as the agent performing them becomes better at prediction? And likewise for planning. If prediction and planning are the two fundamental components of means-ends reasoning, and both have recalcitrance that increases significantly with the intelligence of the agent performing them, then we have reason to reject Bostrom’s core argument and assign a very low probability to the doomsday scenario that occupies much of Bostrom’s imagination in Superintelligence. If this is the case, that suggests we should be devoting resources to anticipating what he calls multipolar scenarios, where no intelligent system has a decisive strategic advantage, instead.

Instrumentality run amok: Bostrom and Instrumentality

Narrowing our focus onto the crux of Bostrom’s argument, we can see how tightly it is bound to a much older philosophical notion of instrumental reason. This comes to the forefront in his discussion of the orthogonality thesis (p.107):

The orthogonality thesis
Intelligence and final goals are orthogonal: more or less any level of intelligence could in principle be combined with more or less any final goal.

Bostrom goes on to clarify:

Note that the orthogonality thesis speaks not of rationality or reason, but of intelligence. By “intelligence” we here mean something like skill at prediction, planning, and means-ends reasoning in general. This sense of instrumental cognitive efficaciousness is most relevant when we are seeking to understand what the causal impact of a machine superintelligence might be.

Bostrom maintains that the generality of instrumental intelligence, which I would argue is evinced by the generality of computing, gives us a way to predict how intelligent systems will act. Specifically, he says that an intelligent system (and specifically a superintelligent) might be predictable because of its design, because of its inheritance of goals from a less intelligence system, or because of convergent instrumental reasons. (p.108)

Return to the core logic of Bostrom’s argument. The existential threat posed by superintelligence is simply that the instrumental intelligence of an intelligent system will invest in itself and overwhelm any ability by us (its well-intentioned creators) to control its behavior through design or inheritance. Bostrom thinks this is likely because instrumental intelligence (“skill at prediction, planning, and means-ends reasoning in general”) is a kind of resource or capacity that can be accumulated and put to other uses more widely. You can use instrumental intelligence to get more instrumental intelligence; why wouldn’t you? The doomsday prophecy of a fast takeoff superintelligence achieving a decisive strategic advantage and becoming a universe-dominating singleton depends on this internal cycle: instrumental intelligence investing in itself and expanding exponentially, assuming low recalcitrance.

This analysis brings us to a significant focal point. The critical missing formula in Bostrom’s argument is (specifically) the recalcitrance function of instrumental intelligence. This is not the same as recalcitrance with respect to “general” intelligence or even “super” intelligence. Rather, what’s critical is how much a process dedicated to “prediction, planning, and means-ends reasoning in general” can improve its own capacities at those things autonomously. The values of this recalcitrance function will bound the speed of superintelligence takeoff. These bounds can then inform the optimal allocation of research funding towards anticipation of future scenarios.

In what I hope won’t distract from the logical analysis of Bostrom’s argument, I’d like to put it in a broader context.

Take a minute to think about the power of general purpose computing and the impact it has had on the past hundred years of human history. As the earliest digital computers were informed by notions of artificial intelligence (c.f. Alan Turing), we can accurately say that the very machine I use to write this text, and the machine you use to read it, are the result of refined, formalized, and materialized instrumental reason. Every programming language is a level of abstraction over a machine that has no ends in itself, but which serves the ends of its programmer (when it’s working). There is a sense in which Bostrom’s argument is not about a near future scenario but rather is just a description of how things already are.

Our very concepts of “technology” and “instrument” are so related that it can be hard to see any distinction at all. (c.f. Heidegger, “The Question Concerning Technology“) Bostrom’s equating of instrumentality with intelligence is a move that makes more sense as computing becomes ubiquitously part of our experience of technology. However, if any instrumental mechanism can be seen as a form of intelligence, that lends credence to panpsychist views of cognition as life. (c.f. the Santiago theory)

Meanwhile, arguably the genius of the market is that it connects ends (through consumption or “demand”) with means (through manufacture and services, or “supply”) efficiently, bringing about the fruition of human desire. If you replace “instrumental intelligence” with “capital” or “money”, you get a familiar critique of capitalism as a system driven by capital accumulation at the expense of humanity. The analogy with capital accumulation is worthwhile here. Much as in Bostrom’s “takeoff” scenarios, we can see how capital (in the modern era, wealth) is reinvested in itself and grows at an exponential rate. Variable rates of return on investment lead to great disparities in wealth. We today have a “multipolar scenario” as far as the distribution of capital is concerned. At times people have advocated for an economic “singleton” through a planned economy.

It is striking that contemporary analytic philosopher and futurist Nick Bostrom’s contemplates the same malevolent force in his apocalyptic scenario as does Max Horkheimer in his 1947 treatise “Eclipse of Reason“: instrumentality run amok. Whereas Bostrom concerns himself primarily with what is literally a machine dominating the world, Horkheimer sees the mechanism of self-reinforcing instrumentality as pervasive throughout the economic and social system. For example, he sees engineers as loci of active instrumentalism. Bostrom never cites Horkheimer, let alone Heidegger. That there is a convergence of different philosophical sub-disciplines on the same problem suggests that there are convergent ultimate reasons which may triumph over convergent instrumental reasons in the end. The question of what these convergent ultimate reasons are, and what their relationship to instrumental reasons is, is a mystery.


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