### “To be great is to be misunderstood.”

A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines. With consistency a great soul has simply nothing to do. He may as well concern himself with his shadow on the wall. Speak what you think now in hard words, and to-morrow speak what to-morrow thinks in hard words again, though it contradict every thing you said to-day. — `Ah, so you shall be sure to be misunderstood.’ — Is it so bad, then, to be misunderstood? Pythagoras was misunderstood, and Socrates, and Jesus, and Luther, and Copernicus, and Galileo, and Newton, and every pure and wise spirit that ever took flesh. To be great is to be misunderstood. –
Emerson, Self-Reliance

Lately in my serious scientific work again I’ve found myself bumping up against the limits of intelligibility. This time, it is intelligibility from within a technical community: one group of scientists who are, I’ve been advised, unfamiliar with another, different technical formalism. As a new entrant, I believe the latter would be useful to understand the domain of the former. But to do this, especially in the context of funders (who need to explain things to their own bosses in very concrete terms), would be unproductive, a waste of precious time.

Reminded by recent traffic of some notes I wrote long ago in frustration at Hannah Arendt, I found something apt about her comments. Science in the mode of what Kuhn calls “normal science” must be intelligible to itself and its benefactors. But that is all. It need not be generally intelligible to other scientists; it need not understand other scientists. It need only be a specialized and self-sustaining practice, a discipline.

Programming (which I still study) is actually quite different from science in this respect. Because software code is a medium used for communication by programmers, and software code is foremost interpreted by a compiler, one relates as a programmer to other programmers differently than the way scientists relate to other scientists. To some extent the productive formal work has moved over into software, leaving science to be less formal and more empirical. This is, in my anecdotal experience, now true even in the fields of computer science, which were once one of the bastions of formalism.

Arendt’s criticism of scientists, that should be politically distrusted because “they move in a world where speech has lost its power”, is therefore not precisely true because scientific operations are, certainly, mediated by language.

But this is normal science. Perhaps the scientists who Arendt distrusted politically were not normal scientists, but rather those sorts of scientists that were responsible for scientific revolutions. These scientist must not have used language that was readily understood by their peers, at least initially, because they were creating new concepts, new ideas.

Perhaps these kinds of scientists are better served by existentialism, as in Nietzsche’s brand, as an alternative to politics. Or by Emerson’s transcendentalism, which Sloterdijk sees as very spiritually kindred to Nietzsche but more balanced.

### A quick recap: from political to individual reasoning about ends

So to recap:

Horkheimer warned in Eclipse of Reason that formalized subjective reason that optimizes means was going to eclipse “objective reason” about social harmony, the good life, the “ends” that really matter. Technical efficacy which is capitalism which is AI would expose how objective reason is based in mythology and so society would be senseless and miserable forever.

There was at one point a critical reaction against formal, technical reason that was called the Science Wars in the 90’s, but though it continues to have intellectual successors it is for the most part self-defeating and powerless. Technical reasoning is powerful because it is true, not true because it is powerful.

It remains an open question whether it’s possible to have a society that steers itself according to something like objective reason. One could argue that Habermas’s project of establishing communicative action as a grounds for legitimate pluralistic democracy was an attempt to show the possibility of objective reason after all. This is, for some reason, an unpopular view in the United States, where democracy is often seen as a way of mediating agonistic interests rather than finding common ones.

But Horkheimer’s Frankfurt School is just one particularly depressing and insightful view. Maybe there is some other way to go. For example, one could decide that society has always been disappointing, and that determining ones true “ends” is an individual, rather than collective, endeavor. Existentialism is one such body of work that posits a substantive moral theory (or at least works at one) that is distrustful of political as opposed to individual solutions.

### Notes on Sloterdijk’s “Nietzsche Apostle”

Fascisms, past and future, are politically nothing than insurrections of energy-charged losers, who, for a time of exception, change the rules in order to appear as victors.
— Peter Sloterdijk, Nietzsche Apostle

Speaking of existentialism, today I finished reading Peter Sloterdijk’s Semiotext(e) issue, “Nietzsche Apostle”. A couple existing reviews can better sum it up than I can. These are just some notes.

Sloterdijk has a clear-headed, modern view of the media and cultural complexes around writing and situates his analysis of Nietzsche within these frames. He argues that Nietzsche created an “immaterial product”, a “brand” of individualism that was a “market maker” because it anticipated what people would crave when they realized they were allowed to want. He does this through a linguistic innovation: blatant self-aggrandizement on a level that had been previously taboo.

One of the most insightful parts of this analysis is Sloterdijk’s understanding of the “eulogistic function” of writing, something about which I have been naive. He’s pointing to the way writing increases its authority by referencing other authorities and borrowing some of their social capital. This was once done, in ancient times, through elaborate praises of kings and ancestors. There have been and continue to be (sub)cultures where references to God or gods or prophets or scriptures give a text authority. In the modern West among the highly educated this is no longer the case. However, in the academy citations of earlier scholars serves some of this function: citing a classic work still gives scholarship some gravitas, though I’ve noted this seems to be less and less the case all the time. Most academic work these days serves its ‘eulogistic function’ in a much more localized way of mutually honoring peers within a discipline and the still living and active professors who might have influence over ones hiring, grants, and/or tenure.

Sloterdijk’s points about the historical significance of Nietzsche are convincing, and he succeeds in building an empathetic case for the controversial and perhaps troubled figure. Sloterdijk also handles most gracefully the dangerous aspects of Nietzsche’s legacy, most notably when in a redacted and revised version his work was coopted by the Nazis. Partly through references to Nietzsche’s text and partly by illustrating the widespread phenomenon of self-serving redactionist uses of hallowed texts (he goes into depth about Jefferson’s bible, for example), he shows that any use of his work to support a movement of nationalist resentment is a blatant misappropriation.

Indeed, Sloterdijk’s discussion of Nietzsche and fascism is prescient for U.S. politics today (I’ve read this volume was based on a lecture in 2000). For Sloterdijk, both far right and far left politics are often “politics of resentment”, which is why it is surprisingly easy for people to switch from one side to the other when the winds and opportunities change. Nietzsche’s famously denounced “herd morality” as that system of morality that deplores the strong and maintains the moral superiority of the weak. In Nietzsche’s day, this view was represented by Christianity. Today, it is (perhaps) represented by secular political progressivism, though it may just as well be represented by those reactionary movements that feed on resentment towards coastal progressive elites. All these political positions that are based on arguments about who is entitled to what and who isn’t getting their fair share are the same for Sloterdijk’s Nietzsche. They miss the existential point.

Rather, Nietzsche advocates for an individualism that is free to pursue self-enhancement despite social pressures to the contrary. Nietzsche is anti-egalitarian, at least in the sense of not prioritizing equality for its own sake. Rather, he proposes a morality that is libertarian without any need for communal justification through social contract or utilitarian calculus. If there is social equality to be had, it is through the generosity of those who have excelled.

This position is bound to annoy the members of any political movement whose modus operandi is mobilization of resentful solidarity. It is a rejection of that motive and tactic in favor of more joyful and immediate freedom. It may not be universally accessible; it does not brand itself that way. Rather, it’s a lifestyle option for “the great”, and it’s left open who may self-identify as such.

Without judging its validity, it must be noted that it is a different morality than those based on resentment or high-minded egalitarianism.

### Existentialism in Design: Comparison with “Friendly AI” research

Turing Test [xkcd]

I made a few references to Friendly AI research in my last post on Existentialism in Design. I positioned existentialism as an ethical perspective that contrasts with the perspective taken by the Friendly AI research community, among others. This prompted a response by a pseudonymous commenter (in a sadly condescending way, I must say) who linked me to a a post, “Complexity of Value” on what I suppose you might call the elite rationalist forum Arbital. I’ll take this as an invitation to elaborate on how I think existentialism offers an alternative to the Friendly AI perspective of ethics in technology, and particularly the ethics of artificial intelligence.

The first and most significant point of departure between my work on this subject and Friendly AI research is that I emphatically don’t believe the most productive way to approach the problem of ethics in AI is to consider the problem of how to program a benign Superintelligence. This is for reasons I’ve written up in “Don’t Fear the Reaper: Refuting Bostrom’s Superintelligence Argument”, which sums up arguments made in several blog posts about Nick Bostrom’s book on the subject. This post goes beyond the argument in the paper to address further objections I’ve heard from Friendly AI and X-risk enthusiasts.

What superintelligence gives researchers is a simplified problem. Rather than deal with many of the inconvenient contingencies of humanity’s technically mediated existence, superintelligence makes these irrelevant in comparison to the limiting case where technology not only mediates, but dominates. The question asked by Friendly AI researchers is how an omnipotent computer should be programmed so that it creates a utopia and not a dystopia. It is precisely because the computer is omnipotent that it is capable of producing a utopia and is in danger of creating a dystopia.

If you don’t think superintelligences are likely (perhaps because you think there are limits to the ability of algorithms to improve themselves autonomously), then you get a world that looks a lot more like the one we have now. In our world, artificial intelligence has been incrementally advancing for maybe a century now, starting with the foundations of computing in mathematical logic and electrical engineering. It proceeds through theoretical and engineering advances in fits and starts, often through the application of technology to solve particular problems, such as natural language processing, robotic control, and recommendation systems. This is the world of “weak AI”, as opposed to “strong AI”.

It is also a world where AI is not the great source of human bounty or human disaster. Rather, it is a form of economic capital with disparate effects throughout the total population of humanity. It can be a source of inspiring serendipity, banal frustration, and humor.

Let me be more specific, using the post that I was linked to. In it, Eliezer Yudkowsky posits that a (presumeably superintelligent) AI will be directed to achieve something, which he calls “value”. The post outlines a “Complexity of Value” thesis. Roughly, this means that the things that we want AI to do cannot be easily compressed into a brief description. For an AI to not be very bad, it will need to either contain a lot of information about what people really want (more than can be easily described) or collect that information as it runs.

That sounds reasonable to me. There’s plenty of good reasons to think that even a single person’s valuations are complex, hard to articulate, and contingent on their circumstances. The values appropriate for a world dominating supercomputer could well be at least as complex.

But so what? Yudkowsky argues that this thesis, if true, has implications for other theoretical issues in superintelligence theory. But does it address any practical questions of artificial intelligence problem solving or design? That it is difficult to mathematically specify all of values or normativity, and that to attempt to do so one would need to have a lot of data about humanity in its particularity, is a point that has been apparent to ethical philosophy for a long time. It’s a surprise or perhaps disappointment only to those who must mathematize everything. Articulating this point in terms of Kolmogorov complexity does not particularly add to the insight so much as translate it into an idiom used by particular researchers.

Where am I departing from this with “Existentialism in Design”?

Rather than treat “value” as a wholly abstract metasyntactic variable representing the goals of a superintelligent, omniscient machine, I’m approaching the problem more practically. First, I’m limiting myself to big sociotechnical complexes wherein a large number of people have some portion of their interactions mediated by digital networks and data centers and, why not, smartphones and even the imminent dystopia of IoT devices. This may be setting my work up for obsolescence, but it also grounds the work in potential action. Since these practical problems rely on much of the same mathematical apparatus as the more far-reaching problems, there is a chance that a fundamental theorem may arise from even this applied work.

That restriction on hardware may seem banal; but it’s a particular philosophical question that I am interested in. The motivation for considering existentialist ethics in particular is that it suggests new kinds of problems that are relevant to ethics but which have not been considered carefully or solved.

As I outlined in a previous post, many ethical positions are framed either in terms of consequentialism, evaluating the utility of a variety of outcomes, or deontology, concerned with the consistency of behavior with more or less objectively construed duties. Consequentialism is attractive to superintelligence theorists because they imagine their AI’s to have to ability to cause any consequence. The critical question is how to give it a specification the leads to the best or adequate consequences for humanity. This is a hard problem, under their assumptions.

Deontology is, as far as I can tell, less interesting to superintelligence theorists. This may be because deontology tends to be an ethics of human behavior, and for superintelligence theorists human behavior is rendered virtually insignificant by superintelligent agency. But deontology is attractive as an ethics precisely because it is relevant to people’s actions. It is intended as a way of prescribing duties to a person like you and me.

With Existentialism in Design (a term I may go back and change in all these posts at some point; I’m not sure I love the phrase), I am trying to do something different.

I am trying to propose an agenda for creating a more specific goal function for a limited but still broad-reaching AI, assigning something to its ‘value’ variable, if you will. Because the power of the AI to bring about consequences is limited, its potential for success and failure is also more limited. Catastrophic and utopian outcomes are not particularly relevant; performance can be evaluated in a much more pedestrian way.

Moreover, the valuations internalized by the AI are not to be done in a directly consequentialist way. I have suggested that an AI could be programmed to maximize the meaningfulness of its choices for its users. This is introducing a new variable, one that is more semantically loaded than “value”, though perhaps just as complex and amorphous.

Particular to this variable, “meaningfulness”, is that it is a feature of the subjective experience of the user, or human interacting with the system. It is only secondarily or derivatively an objective state of the world that can be evaluated for utility. To unpack in into a technical specification, we will require a model (perhaps a provisional one) of the human condition and what makes life meaningful. This very well may include such things as the autonomy, or the ability to make one’s own choices.

I can anticipate some objections along the lines that what I am proposing still looks like a special case of more general AI ethics research. Is what I’m proposing really fundamentally any different than a consequentialist approach?

I will punt on this for now. I’m not sure of the answer, to be honest. I could see it going one of two different ways.

The first is that yes, what I’m proposing can be thought of as a narrow special case of a more broadly consequentialist approach to AI design. However, I would argue that the specificity matters because of the potency of existentialist moral theory. The project of specify the latter as a kind of utility function suitable for programming into an AI is in itself a difficult and interesting problem without it necessarily overturning the foundations of AI theory itself. It is worth pursuing at the very least as an exercise and beyond that as an ethical intervention.

The second case is that there may be something particular about existentialism that makes encoding it different from encoding a consequentialist utility function. I suspect, but leave to be shown, that this is the case. Why? Because existentialism (which I haven’t yet gone into much detail describing) is largely a philosophy about how we (individually, as beings thrown into existence) come to have values in the first place and what we do when those values or the absurdity of circumstances lead us to despair. Existentialism is really a kind of phenomenological metaethics in its own right, one that is quite fluid and resists encapsulation in a utility calculus. Most existentialists would argue that at the point where one externalizes one’s values as a utility function as opposed to living as them and through them, one has lost something precious. The kinds of things that existentialism derives ethical imperatives from, such as the relationship between one’s facticity and transcendence, or one’s will to grow in one’s potential and the inevitability of death, are not the kinds of things a (limited, realistic) AI can have much effect on. They are part of what has been perhaps quaintly called the human condition.

To even try to describe this research problem, one has to shift linguistic registers. The existentialist and AI research traditions developed in very divergent contexts. This is one reason to believe that their ideas are new to each other, and that a synthesis may be productive. In order to accomplish this, one needs a charitably considered, working understanding of existentialism. I will try to provide one in my next post in this series.

### “The Microeconomics of Complex Economies”

I’m dipping into The microeconomics of complex economies: Evolutionary, institutional, neoclassical, and complexity perspectives, by Elsner, Heinrich, and Schwardt, all professors at the University of Bremen.

It is a textbook, as one would teach a class from. It is interesting because it is self-consciously written as a break from neoclassical microeconomics. According to the authors, this break had been a long time coming but the last straw was the 2008 financial crisis. This at last, they claim, showed that neoclassical faith in market equilibrium was leaving something important out.

Meanwhile, “heterodox” economics has been maturing for some time in the economics blogosphere, while complex systems people have been interested in economics since the emergence of the field. What Elsner, Heinrich, and Schwardt appear to be doing with this textbook is providing a template for an undergraduate level course on the subject, legitimizing it as a discipline. They are not alone. They cite Bowles’s Microeconomics as worthy competition.

I have not yet read the chapter of the Elsner, Heinirch, and Schwardt book that covers philosophy of science and its relationship to the validity of economics. It looks from a glance at it very well done. But I wanted to note my preliminary opinion on the matter given my recent interest in Shapiro and Varian‘s information economics and their claim to be describing ‘laws of economics’ that provide a reliable guide to business strategy.

In brief, I think Shapiro and Varian are right: they do outline laws of economics that provide a reliable guide to business strategy. This is in fact what neoclassical economics is good for.

What neoclassical economics is not always great at is predicting aggregate market behavior in a complex world. It’s not clear if any theory could ever be good at predicting aggregate market behavior in a complex world. It is likely that if there were one, it would be quickly gamed by investors in a way that would render it invalid.

Given vast information asymmetries it seems the best one could hope for is a theory of the market being able to assimilate the available information and respond wisely. This is the Hayekian view, and it’s not mainstream. It suffers the difficulty that it is hard to empirically verify that a market has performed optimally given that no one actor, including the person attempting the verify Hayekian economic claims, has all the information to begin with. Meanwhile, it seems that there is no sound a priori reason to believe this is the case. Epstein and Axtell (1996) have some computational models where they test when agents capable of trade wind up in an equilibrium with market-clearing prices and in their models this happens under only very particular an unrealistic conditions.

That said, predicting aggregate market outcomes is a vastly different problem than providing strategic advice to businesses. This is the point where academic critiques of neoclassical economics miss the mark. Since phenomena concerning supply and demand, pricing and elasticity, competition and industrial organization, and so on are part of the lived reality of somebody working in industry, formalizations of these aspects of economic life can be tested and propagated by many more kinds of people than the phenomena of total market performance. The latter is actionable only for a very rare class of policy-maker or financier.

References

Bowles, S. (2009). Microeconomics: behavior, institutions, and evolution. Princeton University Press.

Elsner, W., Heinrich, T., & Schwardt, H. (2014). The microeconomics of complex economies: Evolutionary, institutional, neoclassical, and complexity perspectives. Academic Press.

Epstein, Joshua M., and Robert Axtell. Growing artificial societies: social science from the bottom up. Brookings Institution Press, 1996.

### Existentialism in Design: Motivation

There has been a lot of recent work on the ethics of digital technology. This is a broad area of inquiry, but it includes such topics as:

• The ethics of Internet research, including the Facebook emotional contagion study and the Encore anti-censorship study.
• Fairness, accountability, and transparnecy in machine learning.
• Algorithmic price-gauging.
• Autonomous car trolley problems.
• Ethical (Friendly?) AI research? This last one is maybe on the fringe…

If you’ve been reading this blog, you know I’m quite passionate about the intersection of philosophy and technology. I’m especially interested in how ethics can inform the design of digital technology, and how it can’t. My dissertation is exploring this problem in the privacy engineering literature.

I have a some dissatisfaction towards this field which I don’t expect to make it into my dissertation. One is that the privacy engineering literature and academic “ethics of digital technology” more broadly tends to be heavily informed by the law, in the sense of courts, legislatures, and states. This is motivated by the important consideration that technology, and especially technologists, should in a lot of cases be compliant with the law. As a practical matter, it certainly spares technologists the trouble of getting sued.

However, being compliant with the law is not precisely the same things as being ethical. There’s a long ethical tradition of civil disobedience (certain non-violent protest activities, for example) which is not strictly speaking legal though it has certainly had impact on what is considered legal later on. Meanwhile, the point has been made but maybe not often enough that legal language often looks like ethical language, but really shouldn’t be interpreted that way. This is a point made by Oliver Wendell Holmes Junior in his notable essay, “The Path of the Law”.

When the ethics of technology are not being framed in terms of legal requirements, they are often framed in terms of one of two prominent ethical frameworks. One framework is consequentialism: ethics is a matter of maximizing the beneficial consequences and minimizing the harmful consequences of ones actions. One variation of consequentialist ethics is utilitarianism, which attempts to solve ethical questions by reducing them to a calculus over “utility”, or benefit as it is experienced or accrued by individuals. A lot of economics takes this ethical stance. Another, less quantitative variation of consequentialist ethics is present in the research ethics principle that research should maximize benefits and minimize harms to participants.

The other major ethical framework used in discussions of ethics and technology is deontological ethics. These are ethics that are about rights, duties, and obligations. Justifying deontological ethics can be a little trickier than justifying consequentialist ethics. Frequently this is done by invoking social norms, as in the case of Nissenbaum’s contextual integrity theory. Another variation of a deontological theory of ethics is Habermas’s theory of transcendental pragmatics and legitimate norms developed through communicative action. In the ideal case, these norms become encoded into law, though it is rarely true that laws are ideal.

Consequentialist considerations probably make the world a better place in some aggregate sense. Deontological considerations probably maybe the world a fairer or at least more socially agreeable place, as in their modern formulations they tend to result from social truces or compromises. I’m quite glad that these frameworks are taken seriously by academic ethicists and by the law.

However, as I’ve said I find these discussions dissatisfying. This is because I find both consequentialist and deontological ethics to be missing something. They both rely on some foundational assumptions that I believe should be questioned in the spirit of true philosophical inquiry. A more thorough questioning of these assumptions, and tentative answers to them, can be found in existentialist philosophy. Existentialism, I would argue, has not had its due impact on contemporary discourse on ethics and technology, and especially on the questions surrounding ethical technical design. This is a situation I intend to one day remedy. Though Zach Weinersmith has already made a fantastic start:

“Self Driving Car Ethics”, by Weinersmith

SMBC: Autonomous vehicle ethics

What kinds of issues would be raised by existentialism in design? Let me try out a few examples of points made in contemporary ethics of technology discourse and a preliminary existentialist response to them.

Ethical Charge Existentialist Response
A superintelligent artificial intelligence could, if improperly designed, result in the destruction or impairment of all human life. This catastrophic risk must be avoided. (Bostrom, 2014) We are all going to die anyway. There is no catastrophic risk; there is only catastrophic certainty. We cannot make an artificial intelligence that prevents this outcome. We must instead design artificial intelligence that makes life meaningful despite its finitude.
Internet experiments must not direct the browsers of unwitting people to test the URLs of politically sensitive websites. Doing this may lead to those people being harmed for being accidentally associated with the sensitive material. Researchers should not harm people with their experiments. (Narayanan and Zevenbergen, 2015) To be held responsible by a state’s criminal justice system for the actions taken by ones browser, controlled remotely from America, is absurd. This absurdity, which pervades all life, is the real problem, not the suffering potentially caused by the experiment (because suffering in some form is inevitable, whether it is from painful circumstance or from ennui.) What’s most important is the exposure of this absurdity and the potential liberation from false moralistic dogmas that limit human potential.
Use of Big Data to sort individual people, for example in the case of algorithms used to choose among applicants for a job, may result in discrimination against historically disadvantaged and vulnerable groups. Care must be taken to tailor machine learning algorithms to adjust for the political protection of certain classes of people. (Barocas and Selbst, 2016) The egalitarian tendency in ethics which demands that the greatest should invest themselves in the well-being of the weakest is a kind of herd morality, motivated mainly by ressentiment of the disadvantaged who blame the powerful for their frustrations. This form of ethics, which is based on base emotions like pity and envy, is life-negating because it denies the most essential impulse of life: to overcome resistance and to become great. Rather than restrict Big Data’s ability to identify and augment greatness, it should be encouraged. The weak must be supported out of a spirit of generosity from the powerful, not from a curtailment of power.

As a first cut at existentialism’s response to ethical concerns about technology, it may appear that existentialism is more permissive about the use and design of technology than consequentialism and deontology. It is possible that this conclusion will be robust to further investigation. There is a sense in which existentialism may be the most natural philosophical stance for the technologist because a major theme in existentialist thought is the freedom to choose ones values and the importance of overcoming the limitations on ones power and freedom. I’ve argued before that Simone de Beauvoir, who is perhaps the most clear-minded of the existentialists, has the greatest philosophy of science because it respects this purpose of scientific research. There is a vivacity to existentialism that does not sweat the small stuff and thinks big while at the same time acknowledging that suffering and death are inevitable facts of life.

On the other hand, existentialism is a morally demanding line of inquiry precisely because it does not use either easy metaethical heuristics (such as consequentialism or deontology) or the bald realities of the human condition as a stopgap. It demands that we tackle all the hard questions, sometimes acknowledging that they are answerable or answerable only in the negative, and muddle on despite the hardest truths. Its aim is to provide a truer, better morality than the alternatives.

Perhaps this is best illustrated by some questions implied by my earlier “existentialist responses” that address the currently nonexistent field of existentialism in design. These are questions I haven’t yet heard asked by scholars at the intersection of ethics and technology.

• How could we design an artificial intelligence (or, to make it simpler, a recommendation system) that makes the most meaningful choices for its users?
• What sort of Internet intervention would be most liberatory for the people affected by it?
• What technology can best promote generosity from the world’s greatest people as a celebration of power and life?

These are different questions from any that you read about in the news or in the ethical scholarship. I believe they are nevertheless important ones, maybe more important than the ethical questions that are more typically asked. The theoretical frameworks employed by most ethicists make assumptions that obscure what everybody already knows about the distribution of power and its abuses, the inevitability of suffering and death, life’s absurdity and especially the absurdity if moralizing sentiment in the face of the cruelty of reality, and so on. At best, these ethical discussions inform the interpretation and creation of law, but law is not the same as morality and to confuse the two robs morality of what is perhaps most essential component, which is that is grounded meaningfully in the experience of the subject.

In future posts (and, ideally, eventually in a paper derived from those posts), I hope to flesh out more concretely what existentialism in design might look like.

References

Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact.

Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. OUP Oxford.

Narayanan, A., & Zevenbergen, B. (2015). No Encore for Encore? Ethical questions for web-based censorship measurement.

Weinersmith, Z. “Self Driving Car Ethics”. Saturday Morning Breakfast Cereal.

### Market segments and clusters of privacy concerns

One result from earlier economic analysis is that in the cases where personal information is being used to judge the economic value of an agent (such as when they are going to be hired, or offered a loan), the market is divided between those that would prefer more personal information to flow (because they are highly qualified, or highly credit-worthy), and those that would rather information not flow.

I am naturally concerned about whether this microeconomic modeling has any sort of empirical validity. However, there is some corroborating evidence in the literature on privacy attitudes. Several surveys (see references) have discovered that people’s privacy attitudes cluster into several groups, those only “marginally concerned”, the “pragmatists”, and the “privacy fundamentalists”. These groups have, respectively, stronger and stronger views on the restriction of their flow of personal information.

It would be natural to suppose that some of the variation in privacy attitudes has to do with expected outcomes of information flow. I.e., if people are worried that their personal information will make them ineligible for a job, they are more likely to be concerned about this information flowing to potential employers.

I need to dig deeper into the literature to see whether factors like income have been shown to be correlated with privacy attitudes.

References

Ackerman, M. S., Cranor, L. F., & Reagle, J. (1999, November). Privacy in e-commerce: examining user scenarios and privacy preferences. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 1-8). ACM.

B. Berendt et al., “Privacy in E-Commerce: Stated Preferences versus Actual Behavior,” Comm. ACM, vol. 484, pp. 101-106, 2005.

K.B. Sheehan, “Toward a Typology of Internet Users and Online Privacy Concerns,” The Information Soc., vol. 1821, pp. 21-32, 2002.

### Economic costs of context collapse

One motivation for my recent studies on information flow economics is that I’m interested in what the economic costs are when information flows across the boundaries of specific markets.

For example, there is a folk theory of why it’s important to have data protection laws in certain domains. Health care, for example. The idea is that it’s essential to have health care providers maintain the confidentiality of their patients because if they didn’t then (a) the patients could face harm due to this information getting into the wrong hands, such as those considering them for employment, and (b) this would disincentivize patients from seeking treatment, which causes them other harms.

In general, a good approximation of general expectations of data privacy is that data should not be used for purposes besides those for which the data subjects have consented. Something like this was encoded in the 1973 Fair Information Practices, for example. A more modern take on this from contextual integrity (Nissenbaum, 2004) argues that privacy is maintained when information flows appropriately with respect to the purposes of its context.

A widely acknowledged phenomenon in social media, context collapse (Marwick and boyd, 2011; Davis and Jurgenson, 2014), is when multiple social contexts in which a person is involved begin to interfere with each other because members of those contexts use the same porous information medium. Awkwardness and sometimes worse can ensue. These are some of the major ways the world has become aware of what a problem the Internet is for privacy.

I’d like to propose that an economic version of context collapse happens when different markets interfere with each other through network-enabled information flow. The bogeyman of Big Brother through Big Data, the company or government that has managed to collect data about everything about you in order to infer everything else about you, has as much to do with the ways information is being used in cross-purposed ways as it has to do with the quantity or scope of data collection.

It would be nice to get a more formal grip on the problem. Since we’ve already used it as an example, let’s try to model the case where health information is disclosed (or not) to a potential employer. We already have the building blocks for this case in our model of expertise markets and our model of labor markets.

There are now two uncertain variables of interest. First, let’s consider a variety of health treatments $J$ such that $m = \vert J \vert$. The distribution of health conditions in society is distributed such that the utility of a random person $i$ receiving a treatment $j$ is $w_{i,j}$. Utility for one treatment is not independent from utility from another. So in general $\vec{w} \sim W$, meaning a person’s utility for all treatments is sampled from an underlying distribution $W$.

There is also the uncertain variable of how effective somebody will be at a job they are interested in. We’ll say this is distributed according to $X$, and that a person’s aptitude for the job is $x_i \sim X$.

We will also say that $W$ and $X$ are not independent from each other. In this model, there are certain health conditions that are disabling with respect to a job, and this has an effect on expected performance.

I must note here that I am not taking any position on whether or not employers should take disabilities into account when hiring people. I don’t even know for sure the consequences of this model yet. You could imagine this scenario taking place in a country which does not have the Americans with Disabilities Act and other legislation that affects situations like this.

As per the models that we are drawing from, let’s suppose that normal people don’t know how much they will benefit from different medical treatments; $i$ doesn’t know $\vec{w}_i$. They may or may not know $x_i$ (I don’t yet know if this matters). What $i$ does know is their symptoms, $y_i \sim Y$.

Let’s say person $x_i$ goes to the doctor, reporting $y_i$, on the expectation that the doctor will prescribe them treatment $\hat{j}$ that maximizes their welfare:

$\hat j = arg \max_{j \in J} E[X_j \vert y]$

Now comes the tricky part. Let’s say the doctor is corrupt and willing to sell the medical records of her patients to her patient’s potential employers. By assumption $y_i$ reveals information both about $w_i$ and $x_i$. We know from our earlier study that information about $x_i$ is indeed valuable to the employer. There must be some price (at least within our neoclassical framework) that the employer is willing to pay the corrupt doctor for information about patient symptoms.

We also know that having potential employers know more about your aptitudes is good for highly qualified applicants and bad for not as qualified applicants. The more information employers know about you, the more likely they will be able to tell if you are worth hiring.

The upshot is that there may be some patients who are more than happy to have their medical records sold off to their potential employers because those particular symptoms are correlated with high job performance. These will be attracted to systems that share their information across medical and employment purposes.

But for those with symptoms correlated with lower job performance, there is now a trickier decision. If doctors are corrupt, it may be that they choose not to reveal their symptoms accurately (or at all) because this information might hurt their chances of employment.

A few more wrinkles here. Suppose it’s true the fewer people will go to corrupt doctors because they suspect or know that information will leak to their employers. If there are people who suspect or know that the information that leaks to their employers will reflect on them favorably, that creates a selection effect on who goes to the doctor. This means that the information that $i$ has gone to the doctor, or not, is a signal employers can use to discriminate between potential applicants. So to some extent the harms of the corrupt doctors fall on the less able even if they opt out of health care. They can’t opt out entirely of the secondary information effects.

We can also add the possibility that not all doctors are corrupt. Only some are. But if it’s unknown which doctors are corrupt, the possibility of corruption still affects the strategies of patients/employees in a similar way, only now in expectation. Just as in the Akerlof market for lemons, a few corrupt doctors ruins the market.

I have not made these arguments mathematically specific. I leave that to a later date. But for now I’d like to draw some tentative conclusions about what mandating the protection of health information, as in HIPAA, means for the welfare outcomes in this model.

If doctors are prohibited from selling information to employers, then the two markets do not interfere with each other. Doctors can solicit symptoms in a way that optimizes benefits to all patients. Employers can make informed choices about potential candidates through an independent process. The latter will serve to select more promising applicants from less promising applicants.

But if doctors can sell health information to employers, several things change.

• Employers will benefit from information about employee health and offer to pay doctors for the information.
• Some doctors will discretely do so.
• The possibility of corrupt doctors will scare off those patients who are afraid their symptoms will reveal a lack of job aptitude.
• These patients no longer receive treatment.
• This reduces the demand for doctors, shrinking the health care market.
• The most able will continue to see doctors. If their information is shared with employers, they will be more likely to be hired.
• Employers may take having medical records available to be bought from corrupt doctors as a signal that the patient is hiding something that would reveal poor aptitude.

In sum, without data protection laws, there are fewer people receiving beneficial treatment and fewer jobs for doctors providing beneficial treatment. Employers are able to make more advantageous decisions, and the most able employees are able to signal their aptitude through the corrupt health care system. Less able employees may wind up being identified anyway through their non-participation in the medical system. If that’s the case, they may wind up returning to doctors for treatment anyway, though they would need to have a way of paying for it besides employment.

That’s what this model says, anyway. The biggest surprise for me is the implication that data protection laws serve this interests of service providers by expanding their customer base. That is a point that is not made enough! Too often, the need for data protection laws is framed entirely in terms of the interests of the consumer. This is perhaps a politically weaker argument, because consumers are not united in their political interest (some consumers would be helped, not harmed, by weaker data protection).

References

Akerlof, G. A. (1970). The market for” lemons”: Quality uncertainty and the market mechanism. The quarterly journal of economics, 488-500.

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

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

Nissenbaum, H. (2004). Privacy as contextual integrity. Wash. L. Rev., 79, 119.

### Credit scores and information economics

The recent Equifax data breach brings up credit scores and their role in the information economy. Credit scoring is a controversial topic in the algorithmic accountability community. Frank Pasquale, for example, writes about it in The Black Box Society. Most of the critical writing on the subject points to how credit scoring might be done in a discriminatory or privacy-invasive way. As interesting as those critiques are from a political and ethical perspective, it’s worth reviewing what credit scores are for in the first place.

Let’s model this as we have done in other cases of information flow economics.

There’s a variable of interest, the likelihood that a potential borrower will not default on a loan, $X$. Note that any value sampled from this $x$ will vary within the interval $[0,1]$ because it is a value of probability.

There’s a decision to be made by a bank: whether or not to provide a random borrower a loan.

To keep things very simple, let’s suppose that the bank gets a payoff of $1$ if the borrower is given a loan and does not default and gets a payoff of $-1$ if the borrower gets the loan and defaults. The borrower gets a payoff of $1$ if he gets the loan and $0$ otherwise. The bank’s strategy is to avoid giving loans that lead to negative expected payoff. (This is a gross oversimplification of, but is essentially consistent with, the model of credit used by Blöchlinger and Leippold (2006).

Given a particular $x$, the expected utility of the bank is:

$x (1) + (1 - x) (-1) = 2x - 1$

Given the domain of $[0,1]$, this function ranges from -1 to 1, hitting 0 when $x = .5$.

We can now consider welfare outcomes under conditions of now information flow, total information flow, and partial information flow.

Suppose the bank has no insight into $x$ besides a prior expectation $X$. Then the expected value of the bank upon offering the loan is $E[2x+1]$. If it is above zero, the bank will offer the loan and the borrower gets a positive payoff. If it is below zero, the bank will not offer the loan and both the bank and potential borrower will get zero payoff. The outcome depends entirely on the prior probability of loan default and is either rewards borrowers or not depending on that distribution.

If the bank has total insight into $x$, then the outcomes are different. The bank can use the option to reject borrowers for whom x is less than .5, and accept those for whom x is greater than .5. If we see the game as repeated over many borrowers whose chances of paying off their loan are all sampled from $X$. Then the additional knowledge of the bank creates two classes of potential borrowers, one that gets loans and one that does not. This increases inequality among borrowers.

It also increases the utility of the bank. This is perhaps best illustrated with a simple example. Suppose the distribution $X$ is uniform over the unit interval $[0,1]$. Then the expected value of the bank’s payoff under complete information is

$\int_{.5}^{1} 2x - 1 dx = 0.25$

which is a significant improvement over the expected payoff of 0 in the uninformed case.

Putting off an analysis of the partial information case for now, suffice it to say that we expect partial information (such as a credit score) to lead to an intermediate result, improving bank profits and differentiating borrowers with respect to the bank’s choice to loan.

What is perhaps most interesting about this analysis is the similarity between it and Posner’s employment market. In both cases, the subject of the variable of interest $X$ is a person’s prospects for improving the welfare of the principle decision-maker upon their being selected, where selection also implies benefit to the subject. Uncertainty about the prospects leads to equal treatment of prospective persons and reduced benefit to the principle. More information leads to differentiated impact to the prospects and benefit to the principle.

References

Blöchlinger, A., & Leippold, M. (2006). Economic benefit of powerful credit scoring. Journal of Banking & Finance, 30(3), 851-873.

### Information flow in economics

We have formalized three different cases of information economics:

What we discovered is that each of these cases has, to some extent, a common form. That form is this:

There is a random variable of interest, $x \sim X$ (that is, a value $x$ sampled from a probability distribution $X$), that has direct effect on the welfare outcome of decisions made be agents in the economy. In our cases this was the aptitude of job applicants, consumers willingness to pay, and the utility of receiving a range of different expert recommendations, respectively.

In the extreme cases, the agent at the focus of the economic model could act with extreme ignorance of $x$, or extreme knowledge of it. Generally, the agent’s situation improves the more knowledgeable they are about $x$. The outcomes for the subjects of $X$ vary more widely.

We also considered the possibility that the agent has access to partial information about $X$ through the observation of a different variable $y \sim Y$. Upon observation of $y$, they can make their judgments based on an improved subjective expectation of the unknown variable, $P(x \vert y)$. We assumed that the agent was a Bayesian reasoner and so capable of internalizing evidence according to Bayes rule, hence they are able to compute:

$P(X \vert Y) \propto P(Y \vert X) P(X)$

However, this depends on two very important assumptions.

The first is that the agent knows the distribution $X$. This is the prior in their subjective calculation of the Bayesian update. In our models, we have been perhaps sloppy in assuming that this prior probability corresponds to the true probability distribution from which the value $x$ is drawn. We are somewhat safe in this assumption because for the purposes of determining strategy, only subjective probabilities can be taken into account and we can relax the distribution to encode something close to zero knowledge of the outcome if necessary. In more complex models, the difference between agents with different knowledge of $X$ may be more strategically significant, but we aren’t there yet.

The second important assumption is that the agent knows the likelihood function $P(Y | X)$. This is quite a strong assumption, as it implies that the agent knows truly how Y covaries with X, allowing them to “decode” the message $y$ into useful information about $x$.

It may be best to think of access and usage of the likelihood function as a rare capability. Indeed, in our model of expertise, the assumption was that the service provider (think doctor) knew more about the relationship between $X$ (appropriate treatment) and $Y$ (observable symptoms) than the consumer (patient) did. In the case of companies that use data science, the idea is that some combination of data and science gives the company an edge in knowing the true value of some uncertain property than its competitors.

What we are discovering is that it’s not just the availability of $y$ that matters, but also the ability to interpret $y$ with respect to the probability of $x$. Data does not speak for itself.

This incidentally ties in with a point which we have perhaps glossed over too quickly in the present discussion, which is what is information, really? This may seem like a distraction in a discussion about economics but it is a question that’s come up in my own idiosyncratic “disciplinary” formation. One of the best intuitive definitions of information is provided by philosopher Fred Dretske (1981; 1983). Made a presentation of Fred Dretske’s view on information and its relationship to epistemological skepticism and Shannon information theory; you can find this presentation here. But for present purposes I want to call attention to his definition of what it means for a message to carry information, which is:

[A] message carries the information that X is a dingbat, say, if and only if one could learn (come to know) that X is a dingbat from the message.

When I say that one could learn that X was a dingbat from the message, I mean, simply, that the message has whatever reliable connection with dingbats is required to enable a suitably equipped, but otherwise ignorant receiver, to learn from it that X is a dingbat.

This formulation is worth mentioning because it supplies a kind of philosophical validation for our Bayesian formulation of information flow in the economy. We are modeling situations where Y is a signal that is reliably connected with X such that instantiations of Y carry information about the value of the X. We might express this in terms of conditional entropy:

$H(X|Y) < H(X)$

While this is sufficient for Y to carry information about X, it is not sufficient for any observer of Y to consequently know X. An important part of Dretske's definition is that the receiver must be suitably equipped to make the connection.

In our models, the “suitably equipped” condition is represented as the ability to compute the Bayesian update using a realistic likelihood function $P(Y \vert X)$. This is a difficult demand. A lot of computational statistics has to do with the difficulty of tractably estimating the likelihood function, let alone computing it perfectly.

References

Dretske, F. I. (1983). The epistemology of belief. Synthese, 55(1), 3-19.

Dretske, F. (1981). Knowledge and the Flow of Information.