Digifesto

On descent-based discrimination (a reply to Hanna et al. 2020)

In what is likely to be a precedent-setting case, California regulators filed a suit in the federal court on June 30 against Cisco Systems Inc, alleging that the company failed to prevent discrimination, harassment and retaliation against a Dalit engineer, anonymised as “John Doe” in the filing.

The Cisco case bears the burden of making anti-Dalit prejudice legible to American civil rights law as an extreme form of social disability attached to those formerly classified as “Untouchable.” Herein lies its key legal significance. The suit implicitly compares two systems of descent-based discrimination – caste and race – and translates between them to find points of convergence or family resemblance.

A. Rao, link

There is not much I can add to this article about caste-based discrimination in the U.S. In the law suit, a team of high caste South Asians in California is alleged to have discriminated against a Dalit engineer coworker. The work of the law suit is to make caste-based discrimination legible to American civil rights law. It, correctly, in my view, draws the connection to race.

This illustrative example prompts me to respond to Hanna et al.’s 2020 “Towards a critical race methodology in algorithmic fairness.” This paper by a Google team included a serious, thoughtful consideration of the argument I put forward with my co-author Bruce Haynes in “Racial categories in machine learning”. I like the Hanna et al. paper, think it makes interesting and valid points about the multidimensionality of race, and am grateful for their attention to my work.

I also disagree with some of their characterization of our argument and one of the positions they take. For some time I’ve intended to write a response. Now is a fine time.

First, a quibble: Hanna et al. describe Bruce D. Haynes as a “critical race scholar” and while he may have changed his mind since our writing, at the time he was adamant (in conversation) that he is not a critical race scholar, but that “critical race studies” refers to a specific intellectual project of racial critique that just happens to be really trendy on Twitter. There are lots and lots of other ways to study race critically that are not “critical race studies”. I believe this point was important to Bruce as a matter of scholarly identity. I also feel that it’s an important point because, frankly, I don’t find a lot of “critical race studies” scholarship persuasive and I probably wouldn’t have collaborated as happily with somebody of that persuasion.

So that fact that Hanna et al. explicitly position their analysis in “critical race” methods is a signpost that they are actually trying to accomplish a much more specifically disciplinarily informed project than we were. Sadly, they did not get into the question of how “critical race methodology” differs from other methodologies one might use to study race. That’s too bad, as it supports what I feel is a stifling hegemony that particular discourse has over discussions of race and technology.

The Google team is supportive of the most important contribution of our paper–that racial categories are problematic and that this needs to be addressed in the fairness in AI literature. They then go on to argue against out proposed solution of “using an unsupervised machine learning method to create race-like categories which aim to address “historical racial segregation with reproducing the political construction of racial categories.”” (their rendering). I will defend our solution here.

Their first claim:

First, it would be a grave error to supplant the existing categories of race with race-like categories inferred by unsupervised learning methods. Despite the risk of reifying the socially constructed idea called race, race does exist in the world, as a way of mental sorting, as a discourse which is adopted, as a social thing which has both structural and ideological components. In other words, although race is social constructed, race still has power. To supplant race with race-like categories for the purposes of measurement sidesteps the problem.

This paragraph does feel very “critical race studies” to me, in that it makes totalizing claims about the work race does in society in a way that precludes the possibility of any concrete or focused intervention. I think they misunderstand our proposal in the following ways:

  • We are not proposing that, at a societal and institutional level, we institute a new, stable system of categories derived from patterns of segregation. We are proposing that, ideally, temporary quasi-racial categories are derived dynamically from data about segregation in a way that destabilizes the social mechanisms that reproduce racial hierarchy, reducing the power of those categories.
  • This is proposed as an intervention to be adopted by specific technical systems, not at the level of hegemonic political discourse. It is a way of formulating an anti-racist racial project by undermining the way categories are maintained.
  • Indeed, the idea is to sidestep the problem, in the sense that it is an elegant way to reduce the harm that the problem does. Sidestepping is, imagine it, a way of avoiding a danger. In this case, that danger is the reification of race in large scale digital platforms (for example).

Next, they argue:

Second, supplanting race with race-like categories depends highly on context, namely how race operates within particular systems of inequality and domination. Benthall and Haynes restrict their analysis to that of spatial segregation, which is to be sure, an important and active research area and subject of significant policy discussion (e.g. [76, 99]). However, that metric may appear illegible to analyses pertaining to other racialized institutions, such as the criminal justice system, education, or employment (although one can readily see their connections and interdependencies). The way that race matters or pertains to particular types of structural inequality depends on that context and requires its own modes of operationalization

Here, the Google team takes the anthropological turn and, like many before them, suggests that a general technical proposal is insufficient because it is not sufficiently contextualized. Besides echoing the general problem of the ineffectualness of anthropological methods in technology ethics, they also mischaracterize our paper by saying we restrict our analysis to spatial segregation. This is not true: in the paper we generalize our analysis to social segregation, as in on a social network graph. Naturally, we would be (a) interested in and open to other systems of identifying race as a feature of social structure, and (b) would want to tailor data over which any operationalization technique was applied, where appropriate, to technical and functional context. At the same time, we are on quite solid ground in saying that racial is structural and systemic, and in a sense defined at a holistic societal level as much as it has ramifications in, and is impacted by, the micro- and contextual level as well. As we are approaching the problem from a structural sociological one, we can imagine a structural technical solution. This is an advantage of the method over a more anthropological one.

Third:

At the same time we focus on the ontological aspects of race (what is race, how is it constituted and imagined in the world), it is necessary to pay attention to what we do with race and measures which may be interpreted as race. The creation of metrics and indicators which are race-like will still be interpreted as race.

This is a strange criticism given that one of the potential problems with our paper is that the quasi-racial categories we propose are not interpretable. The authors seem think that our solution involves the institution of new quasi-racial categories at the level of representation or discourse. That’s not what we’ve proposed. We’ve proposed a design for a machine learning system which, we’d hope, would be understood well enough by its engineers to work as an intervention. Indeed, the correlation of the quasi-racial categories with socially recognized racial ones is important if they are to ground fairness interventions; the purpose of our proposed solution is narrowly to allow for these interventions without the reification of the categories.

Enough defense. There is a point the Google team insists on which strikes me as somewhat odd and to me signals a further weakness of their hyper contextualized method: its inability to generalize beyond the hermeneutic cycles of “critical race theory”.

Hanna et al. list several (seven) different “dimensions of race” based on different ways race can be ascribed, inferred, or expressed. There is, here, the anthropological concern with the individual body and its multifaceted presentations in the complex social field. But they explicitly reject one of the most fundamental ways in which race operates at a transpersonal and structural level, which is through families and genealogy. This is well-intentioned but ultimately misguided.

Note that we have excluded “racial ancestry” from this table. Genetics, biomedical researchers, and sociologists of science have criticized the use of “race” to describe genetic ancestry within biomedical research [40, 49, 84, 122], while others have criticized the use of direct-to-consumer genetic testing and its implications for racial and ethnic identification [15, 91, 113]

In our paper, we take pains to point out responsibly how many aspects of racial, such as phenotype, nationality (through citizenship rules), and class signifiers (through inheritance) are connected with ancestry. We, of course, do not mean to equate ancestry with race. Nor, especially, are we saying that there are genetic racialized qualities besides perhaps those associated with phenotype. We are also not saying that direct-to-consumer genetic test data is what institutions should be basing their inference of quasi-racial categories on. Nothing like that.

However, speaking for myself, I believe that an important aspect of how race functions at a social structural level is how it implicates relations of ancestry. A. Rao perhaps puts the point better: race is a system of inherited privilege, and racial discrimination is more often than not discrimination based on descent.

Understanding this about race allows us to see what race has in common with other systems of categorical inequality, such as the caste system. And here was a large part of the point of offering an algorithmic solution: to suggest a system for identifying inequality that transcends the logic of what is currently recognized within the discourse of “critical race theory” and anticipates forms of inequality and discrimination that have not yet been so politically recognized. This will become increasingly an issue when a pluralistic society (or user base of an on-line platform) interacts with populations whose categorical inequalities have different histories and origins besides the U.S. racial system. Though our paper used African-Americans as a referent group, the scope of our proposal was intentionally much broader.

References

Benthall, S., & Haynes, B. D. (2019, January). Racial categories in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 289-298).

Hanna, A., Denton, E., Smart, A., & Smith-Loud, J. (2020, January). Towards a critical race methodology in algorithmic fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 501-512).

Notes about “Data Science and the Decline of Liberal Law and Ethics”

Jake Goldenfein and I have put up on SSRN our paper, “Data Science and the Decline of Liberal Law and Ethics”. I’ve mentioned it on this blog before as something I’m excited about. It’s also been several months since we’ve finalized it, and I wanted to quickly jot some notes about it based on considerations going into it and since then.

The paper was the result of a long and engaged collaboration with Jake which started from a somewhat different place. We considered the question, “What is sociopolitical emancipation in the paradigm of control?” That was a mouthful, but it captured what we were going for:

  • Like a lot of people today, we are interested in the political project of freedom. Not just freedom in narrow, libertarian senses that have proven to be self-defeating, but in broader senses of removing social barriers and systems of oppression. We were ambivalent about the form that would take, but figured it was a positive project almost anybody would be on board with. We called this project emancipation.
  • Unlike a certain prominent brand of critique, we did not begin from an anthropological rejection of the realism of foundational mathematical theory from STEM and its application to human behavior. In this paper, we did not make the common move of suggesting that the source of our ethical problems is one that can be solved by insisting on the terminology or methodological assumptions of some other discipline. Rather, we took advances in, e.g., AI as real scientific accomplishments that are telling us how the world works. We called this scientific view of the world the paradigm of control, due to its roots in cybernetics.

I believe our work is making a significant contribution to the “ethics of data science” debate because it is quite rare to encounter work that is engaged with both project. It’s common to see STEM work with no serious moral commitments or valence. And it’s common to see the delegation of what we would call emancipatory work to anthropological and humanistic disciplines: the STS folks, the media studies people, even critical X (race, gender, etc.) studies. I’ve discussed the limitations of this approach, however well-intentioned, elsewhere. Often, these disciplines argue that the “unethical” aspect of STEM is because of their methods, discourses, etc. To analyze things in terms of their technical and economic properties is to lose the essence of ethics, which is aligned with anthropological methods that are grounded in respectful, phenomenological engagement with their subjects.

This division of labor between STEM and anthropology has, in my view (I won’t speak for Jake) made it impossible to discuss ethical problems that fit uneasily in either field. We tried to get at these. The ethical problem is instrumentality run amok because of the runaway economic incentives of private firms combined with their expanded cognitive powers as firms, a la Herbert Simon.

This is not a terribly original point and we hope it is not, ultimately, a fringe political position either. If Martin Wolf can write for the Financial Times that there is something threatening to democracy about “the shift towards the maximisation of shareholder value as the sole goal of companies and the associated tendency to reward management by reference to the price of stocks,” so can we, and without fear that we will be targeted in the next red scare.

So what we are trying to add is this: there is a cognitivist explanation for why firms can become so enormously powerful relative to individual “natural persons”, one that is entirely consistent with the STEM foundations that have become dominant in places like, most notably, UC Berkeley (for example) as “data science”. And, we want to point out, the consequences of that knowledge, which we take to be scientific, runs counter to the liberal paradigm of law and ethics. This paradigm, grounded in individual autonomy and privacy, is largely the paradigm animating anthropological ethics! So we are, a bit obliquely, explaining why the the data science ethics discourse has gelled in the ways that it has.

We are not satisfied with the current state of ‘data science ethics’ because to the extent that they cling to liberalism, we fear that they miss and even obscure the point, which can best be understood in a different paradigm.

We left as unfinished the hard work of figuring out what the new, alternative ethical paradigm that took cognitivism, statistics, and so on seriously would look like. There are many reasons beyond the conference publication page limit why we were unable to complete the project. The first of these is that, as I’ve been saying, it’s terribly hard to convince anybody that this is a project worth working on in the first place. Why? My view of this may be too cynical, but my explanations are that either (a) this is an interdisciplinary third rail because it upsets the balance of power between different academic departments, or (b) this is an ideological third rail because it successfully identifies a contradiction in the current sociotechnical order in a way that no individual is incentivized to recognize, because that order incentivizes individuals to disperse criticism of its core institutional logic of corporate agency, or (c) it is so hard for any individual to conceive of corporate cognition because of how it exceeds the capacity of human understanding that speaking in this way sounds utterly speculative to a lot of fo people. The problem is that it requires attributing cognitive and adaptive powers to social forms, and a successful science of social forms is, at best, in the somewhat gnostic domain of complex systems research.

The latter are rarely engaged in technology policy but I think it’s the frontier.

References

Benthall, Sebastian and Goldenfein, Jake, Data Science and the Decline of Liberal Law and Ethics (June 22, 2020). Ethics of Data Science Conference – Sydney 2020 (forthcoming). Available at SSRN: https://ssrn.com/abstract=

from morality to economics: some stuff about Marx for Tapan Parikh

I work on a toolkit for heterogeneous agent structural modeling in Economics, Econ-ARK. In this capacity, I work with the project’s creators, who are economists Chris Carroll and Matt White. I think this project has a lot of promise and am each day more excited about its potential.

I am also often in academic circles where it’s considered normal to just insult the entire project of economics out of hand. I hear some empty, shallow snarking economists about once every two weeks. I find this kind of professional politics boring and distracting. It’d also often ignorant. I wanted to connect a few dots to try to remedy the situation, while also noting some substantive points that I think fill out some historical context.

Tracking back to this discussion of morality in the Western philosophical tradition and what challenges it today, the focal character there was Immanuel Kant, who for the sake of argument espoused a model of morality based on universal properties of a moral agent.

Tapan Parikh has argued (in personal communications) that I am “a dumb ass” for using Kant in this way, because Kant is on the record for writing some very racist things. I feel I have to address this point. No, I’m not going to stop working with the ideas from the Western philosophical canon just because so many of them were racist. I’m not a cancel culturist in any sense. I agree with Dave Chappelle on the subject of Louis C.K., for example.

However, it is actually essential to know whether or not racism is a substantive, logical problem with Kant’s philosophy. I’ll defer to others on this point. A quick Googling of the topic seems to indicate that either: Kant was inconsistent, and was a racist while also espousing universalist morality, and that tells us more about Kant the person than it does about universalist morality–the universalist morality transcending Kant’s human failings in this case (Allais, 2016) or Kant actually became less racist during the period in which he was most philosophically productive, which was late in his life (Kleingeld, 2007). I like this latter story better: Kant, being an 18th century German, was racist as hell; then he thought about it a bit harder, developed a universalist moral system, and because, as a consequence, less racist. That seems to be a positive endorsement of what we now call Kantian morality, which is a product of that later period and not the earlier virulently racist period.

Having hopefully settled that question, or at least smoothed it over sufficiently to move on, we can build in more context. Everybody knows this sequence:

Kant -> Hegel -> Marx

Kant starts a transcendent dialectic as a universalist moral project. Hegel historicizes that dialectic, in the process taking into serious consideration the Haitian rebellion, which inspires his account of the Master/Slave dialectic, which is quite literally about slavery and how it is undone by its internal contradictions. The problem, to make a long story short, is that the Master winds up being psychologically dependent on the Slave, and this gives the Slave power over the Master. The Slave’s rebellion is successful, as has happened in history many times. This line of thinking results in, if my notes are right (they might not be) Hegel’s endorsement of something that looks vaguely like a Republic as the end-of-history.

He dies in 1831, and Marx picks up this thread, but famously thinks the historical dialectic is material, not ideal. The Master/Slave dialectic is transposed onto the relationship between Capital and the Proletariat. Capital exploits the Proletariat, but needs the Proletariat. This is what enables the Proletariat to rebel. Once the Proletariat rebel, says Marx, everybody will be on the same level and there will be world peace. I.e., communism is the material manifestation of a universalist morality. This is what Marx inherits from Kant.

But wait, you say. Kant and Hegel were both German Idealists. Where did Marx get this materialist innovation? It was probably his own genius head, you say.

Wrong! Because there’s a thread missing here.

Recall that it was David Hume, a Scotsman, whose provocative skeptical ideas roused Kant from his “dogmatic slumber”. (Historical question: Was it Hume who made Kant “woke” in his old age?) Hume was in the line of Anglophone empiricism, which was getting very bourgey after the Whigs and Locke and all that. Buddies with Hume is Adam Smith who was, let’s not forget, a moral philosopher.

So while Kant is getting very transcendental, Smith is realizing that in order to do any serious moral work you have to start looking at material reality, and so he starts Economics in England.

This next part I didn’t really realize the significance of until digging into it. Smith dies in 1790, just around when Kant is completing the moral project he’s famous for. At that time, the next major figure is 18, coming of age. It’s David Ricardo: a Sephardic Jew turned Unitarian, a Whig, a businessman who makes a fortune speculating on the Battle of Waterloo, who winds up buying a seat in Parliament because you could do that then, and also winds up doing a lot of the best foundational work on economics including inventing the labor theory of value. He was also, incidentally, an abolitionist.

Which means that to complete one’s understanding of Marx, you have to also be thinking:

Hume -> Smith -> Ricardo -> Marx

In other words, Marx is the unlikely marriage of German Idealism, with its continued commitment to universalist ethics, with British empiricism which is–and I keep having to bring this up–weak on ethics. Empiricism is a bad way of building an ethical theory and it’s why the U.S. has bad privacy laws. But it’s a good way to build up an economic materialist view of history. Hence all of Marx’s time looking at factories.

It’s worth noting that Ricardo was also the one who came up with the idea of Land Value Taxation (LVT), which later Henry George popularized as the Single Tax in the late 19th/early 20th century. So Ricardo really is the pivotal figure here in a lot of ways.

In future posts, I hope to be working out more of the background of economics and its connection to moral philosophy. In addition to trying to make the connections to my work on Econ-ARK, there’s also resonances coming up in the policy space. For example, the Law and Political Economy community has been rather explicitly trying to bring back “political economy”–in the sense of Smith, Ricardo, and Marx–into legal scholarship, with a particular aim at regulating the Internet. These threads are braiding together.

References

Allais, L. (2016). Kant’s racism. Philosophical papers45(1-2), 1-36.

Kleingeld, P. (2007). Kant’s second thoughts on race. The Philosophical Quarterly57(229), 573-592.

A philosophical puzzle: morality with complex rationality

There’s a recurring philosophical puzzle that keeps coming up as one drills into the foundational issues at the heart of technology policy. The more complete articulation of it that I know of is in a draft I’ve written with Jake Goldenfein whose publication was COVID delayed. But here is an abbreviated version of the philosophical problem, distilled perhaps from the tech policy context.

For some reason it all comes back to Kant. The categorical imperative has two versions that are supposed to imply each other:

  • Follow rules that would be agreed on as universal by rational beings.
  • Treat others as ends and not means.

This is elegant and worked quite well while the definitions of ‘rationality’ in play were simple enough that Man could stand at the top of the hierarchy.

Kant is outdated now of course but we can see the influence of this theory in Rawls’s account of liberal ethics (the ‘veil of ignorance’ being a proxy for the reasoning being who has transcended their empirical body), in Habermas’s account of democracy (communicative rationality involving the setting aside of individual interests), and so on. Social contract theories are more or less along these lines. This paradigm is still more or less the gold standard.

There’s a few serious challenges to this moral paradigm. They both relate to how the original model of rationality that it is based on is perhaps naive or so rarefied to be unrealistic. What happens if you deny that people are rational in any disinterested sense, or allow for different levels of rationality? It all breaks down.

On the one hand, there’s various forms of egoism. Sloterdijk argues that Nietzsche stood out partly because he argued for an ethics of self-advancement, which rejected deontological duty. Scandalous. The contemporary equivalent is the reputation of Ayn Rand and those inspired by her. The general idea here is the rejection of social contract. This is frustrating to those who see the social contract as serious and valuable. A key feature of this view is that reason is not, as it is for Kant, disinterested. Rather, it is self-interested. It’s instrumental reason with attendant Humean passions to steer it. The passions need not be too intellectually refined. Romanticism, blah blah.

On the other hand, the 20th century discovers scientifically the idea of bounded rationality. Herbert Simon is the pivotal figure here. Individuals, being bounded, form organizations to transcend their limits. Simon is the grand theorist of managerialism. As far as I know, Simon’s theories are amoral, strictly about the execution of instrumental reason.

Nevertheless, Simon poses a challenge to the universalist paradigm because he reveals the inadequacy of individual humans to self-determine anything of significance. It’s humbling; it also threatens the anthropocentrism that provided the grounds for humanity’s mutual self-respect.

So where does one go from here?

It’s a tough question. Some spitballing:

  • One option is to relocate the philosophical subject from the armchair (Kant) to the public sphere (Habermas) into a new kind of institution that was better equipped to support their cogitation about norms. A public sphere equipped with Bloomberg terminals? But then who provides the terminals? And what about actually existing disparities of access?
    • One implication of this option, following Habermas, is that the communications within it, which would have to include data collection and the application of machine learning, would be disciplined in ways that would prevent defections.
    • Another implication, which is the most difficult one, is that the institution that supports this kind of reasoning would have to acknowledge different roles. These roles would constitute each other relationally–there would need to be a division of labor. But those roles would need to each be able to legitimize their participation on the whole and trust the overall process. This seems most difficult to theorize let alone execute.
  • A different option, sort of the unfinished Nietzschean project, is to develop the individual’s choice to defect into something more magnanimous. Simone de Beauvoir’s widely underrated Ethics of Ambiguity is perhaps the best accomplishment along these lines. The individual, once they overcome their own solipsism and consider their true self-interests at an existential level, come to understand how the success of their projects depends on society because society will outlive them. In a way, this point echoes Simon’s in that it begins from an acknowledgment of human finitude. It reasons from there to a theory of how finite human projects can become infinite (achieving the goal of immortality to the one who initiates them) by being sufficiently prosocial.

Either of these approaches might be superior to “liberalism”, which arguably is stuck in the first paradigm (though I suppose there are many liberal theorists who would defend their position). As a thought experiment, I wonder what public policies motivated by either of these positions would look like.

Considering the Endless Frontier Act

As a scientist/research engineer, I am pretty excited about the Endless Frontier Act. Nothing would make my life easier than a big new pile of government money for basic research and technological prototypes awarded to people with PhDs. I’m absolutely all for it and applaud the bipartisan coalition moving it forward.

I am somewhat concerned, however, that the motivation for it is the U.S.’s fear of technological inferiority with respect to China. I’ll take the statement of Dr. Reif, President of MIT, at face value, which is probably foolish given the political acumen and moral flexibility of academic administrators. But look at this:

The COVID-19 pandemic is intensifying U.S. concerns about China’s technological strength. Unfortunately, much of the resulting policy debate has centered on ways to limit China’s capacities — when what we need most is a systematic approach to strengthening our own.

Very straightforward. This is what it’s about. Ok. I get it. You have to sell it to the Trump administration. It’s a slam dunk. But then why write this:

The aim of the new directorate is to support fundamental scientific research — with specific goals in mind. This is not about solving incremental technical problems. As one example, in artificial intelligence, the focus would not be on further refining current algorithms, but rather on developing profoundly new approaches that would enable machines to “learn” using much smaller data sets — a fundamental advance that would eliminate the need to access immense data sets, an area where China holds an immense advantage. Success in this work would have a double benefit: seeding economic benefits for the U.S. while reducing the pressure to weaken privacy and civil liberties in pursuit of more “training” data.

This sounds totally dubious to me. There are well known mathematical theorems addressing why learning without data is impossible. The troublesome fact nodded to is that is because of the political economy of China, it is possible to collect “immense data sets”–specifically about people–without civil liberties issues getting in the way. This presumes that the civil liberties problem with AI is the collection of data from data subjects, not the use of machine learning on those data subjects. But even if you could magically learn about data subjects without collecting data from them, you wouldn’t bypass the civil liberties concerns. Rather, you would have a nightmare world where even sans data collection you could act with godly foresight in one’s interventions on polity. This is a weird fantasy and I’m pretty sure the only reason it’s written this way is to sell the idea superficially to uncritical readers trying to reconcile the various narratives around U.S., technology, and foreign policy which are incoherent.

What it’s really all about, of course, is neoliberalism. Dr. Reif is not shy about this:

The bill would also encourage universities to experiment with new ways to help accelerate the process of bringing innovative ideas to the marketplace, either via established companies or startups. At MIT we started The Engine, an independent entity that provides private-sector funding, work space and technical assistance to start-ups that are developing technologies with enormous potential but that require more extensive technical development than typical VCs will fund, from fusion energy to a fast, inexpensive test for COVID-19. Other models may suit other institutions — but the nation needs to encourage many more such efforts, across the country, to reap the full benefits of our federal investment in science.

The implication here is that unless the results of federal investment in the sciences can be privatized, the country does not “reap the full benefits” of the federal investment. This makes the whole idea of a massively expanded federal government program make a lot more sense, politically, because it’s a massive redistribution of funds to, ultimately, Big Tech, who can buy up any successful ‘startups’ without any downside investment risk. And Big Tech now runs the country and has found a way to equate its global market share with national security such that these things are now indistinguishable in any statement of U.S. policy.

This would all be fine I guess if not for the fact that science is different from technology in that science is, cannot be, a private endeavor. The only way science works is if you have an open vetting process that is constantly arguing with itself and forcing the scientists to reproduce results. This global competition for scientific prestige through the conference and journal systems is what “keeps it honest”, which is precisely what allows it to be credible. (Bourdieu, Science of Science, 2004)

A U.S. strategy since basically the end of World War II has been to lead the scientific field, get first mover advantage on any discoveries, and reap the benefit of being the center of education for global scientific talent through foreign tuition fees and talented immigrants. Then it wields technology transfer as a magic wand for development.

Now this is backfiring a bit because Chinese science students are returning to China to be entrepreneurial there and also work for the government. The U.S. is discovering that science, being an open system, allows others countries to free ride and this is perhaps bothersome to it. The current administration seems to hate the idea of anybody free-riding off of something the U.S. is doing, though in the past those spillover effects (another name for them!) would have been the basis of U.S. leadership. You can’t really have it both ways.

So the renaming of the NSF to the NSTF–with “technology” next to “science”–is concerning because “technology” investment need not be openly vetted. Rather, given the emphasis on go-to-market strategy, it suggests that the scientific norms of reproducibility will be secondary to privatization through intellectual property laws, including trade secrecy. The could be quite bad, because without a disinterested community of people vetting the results, what you’ll probably get is a lot of industrially pre-captured bullshit.

Let’s acknowledge for a minute that the success of most startups little to do with the quality of the technology made and much to do with path dependency in network growth, marketing, and regulatory arbitrage. If the government starts a VC fund run by engineers with no upside then that money goes into a bunch of startups which then compete for creative destruction of each other until one, large enough based on its cannibalizing of the others, gets consumed by by FAANG company. It will, in other words, look like Silicon Valley today, which is not terribly efficient at discovery because success is measured by the market. I.e., because (as Dr. Reif suggests) the return on investment is realized as capital accumulation.

This is all pretty backwards if what you’re trying to do is maintain scientific superiority. Scientific progress requires a functional economy of symbolic capital among scientists operating with intellectual integrity that is “for its own sake”, not operating at the behest of market conquest. The spillover effects and freeriding in science is a feature, not a bug, and it’s difficult to reconcile it with a foreign policy that is paranoid about technology transfer to its competitors. Indeed, this is one reason why scientists are often aligned with humanitarian causes, world peace, etc.

Science is a good social structure with a lot going for it. I hope the new bill pours more money into it without messing it up too much.

Managerialism and Habermas

Managerialism is an “in” topic recently in privacy scholarship (Cohen, 2019; Waldman, 2019). In Waldman’s (2019) formulation, the managerialism problem is, roughly: privacy regulations are written with a certain substantive intent, but the for-profit firms that are the object of these regulations interpret them either as a bothersome constraint on otherwise profitable activity, or else as means to the ends of profitability, efficiency, and so on themselves. In other words, the substance of the regulations are subjugated to the substance of the goals of corporate management. Managerialism.

This is exactly what anybody who has worked in a corporate tech environment would expect. The scholarly accomplishment of presenting these bare facts to a legal academic audience is significant because employees of these corporations are most often locked up by strict NDAs. So while the point is obvious, I mean that in the positive sense that it should be taken as an unquestioned background assumption from now on, not that it shouldn’t have been “discovered” by this field in a different way.

As a “critical” observation, it stands. It raises a few questions:

  • Is this a problem?
  • If so, for whom?
  • If so, what can be done about it?

Here the “critical” method reaches, perhaps, its limits. Notoriously, critical scholarship plays on its own ambiguity, dancing between the positions of “criticism”, or finding of actionable fault, and “critique”, a merely descriptive account that is at most suggestive of action. This ambiguity preserves the standing of the critical scholar. They need never be wrong.

Responding to the situation revealed by this criticism requires a differently oriented kind of work.

Habermas and human interests

A striking about the world of policy and legal scholarship in the United States is that nobody is incentivized to teach or read anything written by past generations, however much it synthesized centuries of knowledge, and so nothing ever changes. For example, arguably, Habermas’s Knowledge and Human Interests (KHI), originally published 1972, arguably lays out the epistemological framework we would want to understand the managerialism issue raised by recent scholars. We should expect Habermas to anticipate the problems raised by capitalism in the 21st century because his points are based on a meticulously constructed, historically informed, universalist, transcendental form of analysis. This sort of analysis is not popular in the U.S.; I have my theories about why. But I digress.

A key point from Habermas (who is summing up and reiterating a lot of other work originating, if it’s possible to say any such thing meaningfully, in Max Weber) is that it’s helpful to differentiate between different kinds of knowledge based on the “human interests” that motivate them. In one formulation (the one in KHI), there are three categories:

  1. The technical interest (from techne) in controlling nature, which leads to the “empirical-analytic”, or positivist, sciences. These correspond to fields like engineering and the positivist social sciences.
  2. The pragmatic interest (from praxis), in mutual understanding which would guide collective action and the formation of norms, leads to the “hermeneutic” sciences. These correspond to fields like history and anthropology and other homes of “interpretivist” methods.
  3. The emancipatory interest, in exposing what has been falsely reified as objective fact as socially contingent. This leads to the critical sciences, which I suppose corresponds to what is today media studies.

This is a helpful breakdown, though I should say it’s not Habermas’s “mature” position, which is quite a bit more complicated. However, it is useful for the purposes of this post because it tracks the managerialist situation raised by Waldman so nicely.

I’ll need to elaborate on one more thing before applying this to the managerialist framing, which is to skip past several volumes of Habermas’s ouvre and get to Theory of Communicative Action, volume II, where he gets to the punchline. By now he’s developed the socially pragmatic interest to be the basis for “communicative rationality”, a discursive discipline in which individual interests are excluded and instead a diversely perspectival but nevertheless measured conversation about the way the social world should normatively be ordered. But where is this field in actuality? Money and power, the “steering media”, are always mussing up this conversation in the “public sphere”. So “public discourse” becomes a very poor proxy for communicative action. Rather–and this is the punchline–the actually existing field of communicative rationality, which is establishing substantive norms while nevertheless being “disinterested” with respect to the individual participants, is the law. That’s what the legal scholarship is for.

Applying the Habermasian frame to managerialism

So here’s what I think is going on. Waldman is pointing out that whereas regulations are being written with a kind of socially pragmatic interest in their impact on the imagined field of discursively rational participants as represented by legal scholarship, corporate managers are operating in the technical mode in order to, say, maximized shareholder profits as is their legally mandated fiduciary duty. And so the meaning of the regulation changes. Because words don’t contain meaning but rather take their meaning from the field in which they operate. A privacy policy that once spoke to human dignity gets misheard and speaks instead to inconvenience of compliance costs and a PR department’s assessment of the competitive benefit of user’s trust.

I suppose this is bothersome from the legal perspective because it’s a bummer when something one feels is an important accomplishment of one’s field is misused by another. But I find the professional politics here, as everywhere, a bit dull and petty.

Crucially, the managerialism problem is not dumb and petty–I wouldn’t be writing all this if I thought so. However, the frustrating aspect of this discourse is that because of the absence of philosophical grounding in this debate, it misses what’s at stake. This is unfortunately characteristic of much American legal analysis. It’s missing because when American scholars address this problem, they do so primarily in the descriptive critical mode, one that is empirical and in a sense positivist, but without the interest in control. This critical mode leads to cynicism. It rarely leads to collective action. Something is missing.

Morality

A missing piece of the puzzle, one which cannot ever be accomplished through empirical descriptive work, is the establishment of the moral consequence of managerialism which is that human beings are being treated as means and not ends, in contradiction with the Kantian categorical imperative, or something like that. Indeed, it is this flavor of moral maxim that threads its way up through Marx into the Frankfurt School literature with all of its well-trod condemnation of instrumental reason and the socially destructive overreach of private capital. This is, of course, what Habermas was going on about in the first place: the steering media, the technical interest, positivist science, etc. as the enemy of politically legitimate praxis based on the substantive recognition of the needs and rights of all by all.

It would be nice, one taking this hard line would say, if all laws were designed with this kind of morality in mind, and if everybody who followed them did so out of a rationally accepted understanding of their import. That would be a society that respected human dignity.

We don’t have that. Instead, we have managerialism. But we’ve known this for some time. All these critiques are effectively mid 20th century.

So now what?

If the “problem” of managerialism is that when regulations reach the firms that they are meant to regulate their meaning changes into an instrumentalist distortion of the original, one might be tempted to try to combat this tendency with an even more forceful use of hermeneutic discourse or an intense training in the social pragmatic stance such that employees of these companies put up some kind of resistance to the instrumental, managerial mindset. That strategy neglects the very real possibility that those employees that do not embrace the managerial mindset will be fired. Only in the most rarified contexts does discourse propel itself with its one force. We must presume that in the corporate context the dominance of managerialist discourse is in part due to a structural selection effect. Good managers lead the company, are promoted, and so on.

So the angle on this can’t be a discursive battle with the employees of regulated firms. Rather, it has to be about corporate governance. This is incidentally absolutely what bourgeois liberal law ought to be doing, in the sense that it’s law as it applies to capital owners. I wonder how long it will be before privacy scholars begin attending to this topic.

References

Benthall, S. (2015). Designing networked publics for communicative action. Interface1(1), 3.

Bohman, J., & Rehg, W. (2007). Jürgen habermas.

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

Habermas, J. (2015). Knowledge and human interests. John Wiley & Sons.

Waldman, A. E. (2019). Privacy Law’s False Promise. Washington University Law Review97(3).

Land value taxation

Henry George’s Progress and Poverty, first published in 1879, is dedicated

TO THOSE WHO, SEEING THE VICE AND MISERY THAT SPRING FROM THE UNEQUAL DISTRIBUTION OF WEALTH AND PRIVILEGE, FEEL THE POSSIBILITY OF A HIGHER SOCIAL STATE AND WOULD STRIVE FOR ITS ATTAINMENT

The book is best known as an articulation of the idea of a “Single Tax [on land]”, a circa 1900 populist movement to replace all taxes with a single tax on land value. This view influence many later land reform and taxation policies around the world; the modern name for this sort of policy is Land Value Taxation (LVT).

The gist of LVT is that the economic value of owning land comes both from the land itself and improvements built on top of it. The value of the underlying land over time is “unearned”–it does not require labor to maintain, it comes mainly from the artificial monopoly right over its use. This can be taxed and redistributed without distorting incentives in the economy.

Phillip Bess’s 2018 article provides an excellent summary of the economic arguments in favor of LVT. Michel Bauwen’s P2P Foundation article summaries where it has been successfully in place. Henry George was an American, but Georgism has been largely an export. General MacArthur was, it has been said, a Georgist, and this accounts for some of the land reform in Asian countries after World War II. Singapore, which owns and rents all of its land, is organized under roughly Georgist principles.

This policy is neither “left” nor “right”. Wikipedia has sprouted an article on geolibertarianism, a term that to me seems a bit sui generis. The 75th-anniversary edition of Progress and Poverty, published 1953, points out that one of the promises of communism is land reform, but it argues that this is a false promise. Rather, Georgist land reform is enlightened and compatible with market freedoms, etc.

I’ve recently dug up my copy of Progress and Poverty and begun to read it. I’m interested in mining it for ideas. What is most striking about it, to a contemporary reader, is the earnest piety of the author. Henry George was clearly a quite religious man, and wrote his lengthy and thorough political-economic analysis of land ownership out of a sincere belief that he was promoting a new world order which would preserve civilization from collapse under the social pressures of inequality.

some PLSC 2020 notes: one framing of the managerialism puzzle

PLSC 2020 was quite interesting this year.

There were a number of threads I’d like to follow up on. One of them has to do with managerialism and the ability of the state (U.S. in this context) to regulate industry.

I need to do some reading to fill some gaps in my understanding, but this is how I understand the puzzle so far.

Suppose the state wants to regulate industry. Congress passes a bill creating an agency with regulatory power with some broadly legislated mandate. The agency comes up with regulations. Businesses then implement policies to comply with the regulation. That’s how it’s supposed to go.

But in practice, there is a lot of translational work being done here. The broadly legislated mandate will be in a language that can get passed by Congress. It delegates elaboration on the specifics to the expert regulators in the agency; these regulators might be lawyers. But when the corporate bosses get the regulations (maybe from their policy staff, also lawyers?) they begin to work with it in a “managerialist” way. This means, I gather, that they manage the transition towards compliance, but in a way that minimizes the costs of compliance. If they can comply without adhering to the purpose of the regulation–which might be ever-so-clear to the lawyers who dreamed it up–so be it.

This seems all quite obvious. Of course it would work this way. If I gather correctly at this point (and maybe I don’t), the managerialist problem is: because of the translational work going on between legislate intent through to administrative regulation into corporate policy into implementation, there’s a lot of potential to have information “lost in translation”, and this information loss works to the advantage of the regulated corporation, because it is using all that lost regulatory bandwidth to its advantage.

We should teach economic history (of data) as “data science ethics”.

I’ve recently come across an interesting paper published at Scipy 2019, Dusen et al.’s “Accelerating the Advancement of Data Science Education” (2019) (link). It summarizes recent trends in data science education, as modeled by UC Berkeley’s Division of Data Science, which is now the Division of Computing, Data Science, and Society (CDSS). This is a striking piece to me as I worked at Berkeley on its data science capabilities several years ago and continue to be fascinated by my alma mater, the School of Information, as it navigates being part of CDSS.

Among other interesting points in the article, two are particularly noteworthy to me. The first is that the integration of data science into the social sciences appears to have continued apace. The article mentions that data science’s integration into the social science has continued apace. Economics, in particular, is well represented and supported in the extended data science curriculum.

The other interesting point is the emphasis on data science ethics as an essential pillar of the educational program. The writing in this piece is consistent with what I’ve come to expect from Berkeley on this topic, and I believe it’s indicative of broad trends in academia.

The authors of this piece are explicit about their “theory of change”. What is data science ethics education supposed to accomplish?

Including training in ethical considerations at all levels of society and all steps of the data science workflow in undergraduate data science curricula could play an important role in stimulating change in industry as our students enter the workforce, perhaps encouraging companies to add ethical standards to their mission statements or to hire chief ethics officers to oversee not only day-to-day operations but also the larger social consequences of their work.

The theory of change articulated by the paper is that industry will change if ethically educated students enter the workforce. They see a future where companies change their mission statements in accord with what has been taught in data science ethics courses, or hire oversight officials.

This is, it must be noted, broadly speculative, and implies that the leadership of the firms who hire these Berkeley grads will be responsive to their employees. However, unlike in some countries in Europe, the United States does not give employees a lot of say in the governance of firms. Technology firms, such as Amazon and Google, have recently proven to be rather unfriendly to employees that attempt to organize in support of “ethics”. This is for highly conventional reasons: the management of these firms tends to be oriented towards the goal of maximizing shareholder profits, and having organized employees advocating for ethical issues that interfere with business is an obstacle to that goal.

This would be understood plainly if economics, or economic history, was taught as part of “data science ethics”. But it’s not for some reason. Information economics, which would presumably be where one would start to investigate the way incentives drive data science institutions, is perhaps too complex to be included in the essential undergraduate curriculum, despite its being perhaps critical to understanding the “data intensive” social world we all live in now.

We forget today, often, that the original economists (Adam Smith, Alfred Marshall, etc.) were all originally moral philosophers. Economics has begun to be seen as a field designed to be in instrumental support of business practice or ideology rather than an investigation into the ethical consequences of social and material structure. That’s too bad.

Instead of teaching economic history, which would be a great way of showing students the ethical implications of technology, instead Berkeley is teaching Science and Technology Studies (STS) and algorithmic fairness! I’ll quote at length:

A recent trend in incorporating such ethical practices includes
incorporating anti-bias algorithms in the workplace. Starting from
the beginning of their undergraduate education, UC Berkeley students can take History 184D: Introduction to Science, Technology, and Society: Human Contexts and Ethics of Data, which covers the implications of computing, such as algorithmic bias. Additionally, students can take Computer Science 294: Fairness in Machine Learning, which spends a semester in resisting racial, political, and physical discrimination. Faculty have also come together to create the Algorithmic Fairness and Opacity Working Group at Berkeley’s School of Information that brainstorms methods to improve algorithms’ fairness, interpretability, and accountability. Implementing such courses and interdisciplinary groups is key to start the conversation within academic institutions, so students
can mitigate such algorithmic bias when they work in industry or
academia post-graduation.


Databases and algorithms are socio-technical objects; they emerge and evolve in tandem with the societies in which they operate [Latour90]. Understanding data science in this way and recognizing its social implications requires a different kind of critical thinking that is taught in data science courses. Issues such as computational agency [Tufekci15], the politics of data classification and statistical inference [Bowker08], [Desrosieres11], and the perpetuation of social injustice through algorithmic decision making [Eubanks19], [Noble18], [ONeil18] are well known to scholars in the interdisciplinary field of science and technology
studies (STS), who should be invited to participate in the development of data science curricula. STS or other courses in the social sciences and humanities dealing specifically with topics related to data science may be included in data science programs.

This is all very typical. The authors are correct that algorithmic fairness and STS have been trendy ways of teaching data science ethics. It is perhaps too cynical to say that these are trendy approaches to “data science ethics” because they are the data science ethics that Microsoft will pay for. Let that slip as a joke.

However, it is unfortunate if students have no better intellectual equipment for dealing with “data science ethics” than this. Algorithmic fairness is a fascinating field of study with many interesting technical results. However, as has been broadly noted by STS scholars, among others, the successful use of “algorithmic fairness” technology depends on the social context in which it is deployed. Often, “fairness” is achieved through greater scientific and technical integrity: for example, properly deducing cause and effect rather than lazily applying techniques that find correlation. But the ethical challenges in the workplace are often not technical challenges. They are the challenges of managing the economic incentives of the firm, and how these effect the power structures within the firm. (Metcalf et al., 2019) This is apparently not material that is being taught at Berkeley to data science students.

This more careful look at the social context in which technology is being used is supposed to be what STS is teaching. But, all too often, this is not what it’s doing. I’ve written elsewhere why STS is not the solution to “tech ethics”. Part of (e.g. Latourian) STS training is a methodological, if not intellectual, relativistic skepticism about science and technology itself (Carroll, 2006). As a consequence, it requires, of itself, to be a humanistic or anthropological field, using “interpretivist” methods, with weak claims to generalizability. It is, first and foremost, an academic field, not an applied one. The purpose of STS is to generate fascinating critiques.

There are many other social sciences that have different aims, such as the aim of building consensus around what social and economic conditions are in order to motivate political change. These social sciences have ethical import. But they are built around a different theory of change. They are aimed at the student as a citizen in a democracy, not as an employee at a company. And while I don’t underestimate the challenges of advocating for designing education to empower students as public citizens in this economic climate, it must nevertheless be acknowledge, as an ethical matter, that a “data science ethics” curriculum that does not address the politics behind those difficulties will be an anemic one, at best.

There is a productive way forward. It requires, however, interdisciplinary thinking that may be uncomfortable or, in the end, impossible for many established institutions. If students are taught a properly historicized and politically substantive “data science ethics”, not in the mode of an STS-based skepticism about technology and science, but rather as economic history that is informed by data science (computational and inferential thinking) as an intellectual foundation, then ethical considerations would need not be relegated to a hopeful afterthought invested in a theory of corporate change that is ultimately a fantasy. Rather, it would put “data science ethics” on a scientific foundation and help civic education justify itself as a matter of social fact.

Addendum: Since the social sciences aren’t doing this work, it looks like some computer scientists are doing it instead. This report by Narayanan provides a recent economic history of “dark patterns” since the 1970’s–an example of how historical research can put “data science ethics” in context.

References

Carroll, P. (2006). Science of Science and Reflexivity. Social Forces85(1), 583-585.

Metcalf, J., & Moss, E. (2019). Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics. Social Research: An International Quarterly86(2), 449-476.

Van Dusen, E., Suen, A., Liang, A., & Bhatnagar, A. (2019). Accelerating the Advancement of Data Science Education. Proceedings of the 18th Python in Science Conference (SciPy 2019)

Considering “Neither Hayek nor Habermas”

I recently came upon an article from 2007, Cass Sunstein’s “Neither Hayek nor Habermas”, arguing that “the blogosphere” would have neither as an effective way of gathering knowledge or as a field for consensus-building. There is no price mechanism, so Hayekian principles do not apply. And there is polarization and what would later be called “echo chambers” to prevent real deliberation.

In an era where online “misinformation” is a household concern, this political analysis seems quite prescient. There never was much reason to expect free digital speech to amount to much besides a warped mirror of the public’s preexisting biases.

A problem with both Hayekian and Habermasian theory, when used this way, is the lack of institutional specificity. The free Web is a plurality of interconnected institutions, with content and traffic flowing constantly between differently designed sociotechnical properties. It is an naivete of all forms of liberal thought that useful social structure will arise spontaneously from the interaction between individuals as though through some magnetic force. Rather, social structures precede and condition the very possibility of personhood and discourse in the first place. “Anyone who says differently is selling something.”

Indeed, despite all the noise on the Internet, there are Hayekian accumulations of information wherever there is the institution of the market. One reason why Amazon has become such a compelling force is because of its effective harnessing of reviews on products. Free speech on the Internet has been just fine for the market.

What about for democracy?

If free digital speech has failed to result in valuable political deliberation, it is wrong to fault the social media platforms. Habermas expected that money and power will distort public discourse; a privately-owned social media platform is a manifestation of this distortion. The locus of valuable political deliberation, therefore, must be in specialized public institutions: most notably, those institutions dedicated to legislation and regulation. In other words, it is the legal system that is, at its best, the site of Habermasian discourse. Not Twitter.

If misinformation on the Internet is “a threat to our democracy”, the problem cannot be solved by changing the content moderation policies on commercial social media platforms. The problem can only be solved by fixing those institutions of public relevance where people’s speech acts matter for public policy.

The closest thing to such a Habermasian institution in the Internet today is perhaps the Request for Comments process on adminstrative regulations in the U.S. There, citizens can freely express their policy ideas and those ideas are, when the system is working, moderated and channeled into nuanced changes to policy.

This somewhat obscure and technocratic government function is overshadowed and sometimes overturned by electoral politics in the U.S., which are at this point anything but deliberative. For various reasons concerning the design of electoral and legislative institutions in the U.S., politics is only superficially discursive. It is in fact a power play, a competition over rents. Under such conditions, we would expect “misinformation” to thrive, because public opinion is mostly inconsequential. There is nothing, pragmatically, to incentivize and ground the hard work of deliberation.

It is perhaps interesting to imagine what kind of self-governing institution would deserve this kind of investment of deliberation.

References

Benthall, Sebastian. “Designing networked publics for communicative action.” Interface 1.1 (2015): 3.

Bruns, Axel. “It’s not the technology, stupid: How the ‘Echo Chamber’and ‘Filter Bubble’metaphors have failed us.” (2019).

Sunstein, Cass R. “Neither Hayek nor Habermas.” Public Choice 134.1-2 (2008): 87-95.