In the last few days, I’ve spent a lot of time reflecting on a remark Susan Halford made at this event about the difference between expertise and discipline. If I understand her correctly, her point was that capacities for knowing and acting in the world (expertise) can have their reproduction organised socially in different ways (discipline) and this is crucial for understanding how knowledge production responds to novel developments. In some cases, discipline might support expertise but in other cases it might hinder it. In either case, expertise is dependent upon it because it requires a social organisation through which existing knowledge is codified, new knowledge incorporated and knowledge practitioners trained. This means that we can’t ever have ‘pure expertise’ as a response to novelty because experts are embedded, even if loosely or unorthodoxly, within disciplines. This is the problem Susan identifies with the politics of discipline generated by big data:

How we define Big Data matters because it shapes our understanding of the expertise that is required to engage with it – to extract the value and deliver the promise. Is this the job for mathematicians and statisticians? Computer scientists? Or ‘domain experts’ – economists, sociologists or geographers – as appropriate to the real-world problems at hand? As the Big Data field forms we see the processes of occupational closure at play: who does this field belong to, who has the expertise, the right to practice? This is of observational interest for those of us who research professions, knowledge and the labour market, as we see how claims to expert knowledge are made by competing disciplines. But it is also of broader interest for those of us concerned with the future of Big Data: the outcome will shape the epistemological foundations of the field. Whether or not it is acknowledged, the disciplinary carve-up of big data will have profound consequences for the questions that are asked, the claims that are made and – ultimately – the value that is derived from this ‘new oil’ in the global economy.

One response to this upheaval is to retreat into disciplinary silos and there’s inevitably a comfort to this. But not only does it cede terrain in a way which might allow narrow forms of expertise to become hegemonic, doubling down on a form of discipline unlikely to survive this transformation of expertise in its current form will inevitably be short sighted. This is how Felicity Callard and Des Fitzgerald describe the shifting plate tectonics of the human sciences in their book on interdisciplinarity:

The more we wander down strange interdisciplinary tracks, the more apparent it becomes to us that being disciplined isn’t playing it safe: the truth is that staying within the narrow epistemological confines of –for example –mid-twentieth-century sociology, while it may produce short-term gains, is not, in fact, the best way to guarantee a career in the twenty-first century (and we mean ‘career’ in its most capacious sense here: we are not using it with the assumption that everyone wants a permanent post at a university, but to express an idea that many would like to find some way to advance their projects, ideas, and so on). The plate tectonics of the human sciences are shifting: we here describe our own forays into one small, circumscribed niche between the social and natural sciences, but expand this horizon to epigenetics, to the emergence of the human microbiome, to all kinds of translational research in mental health, to ‘big data’ and the devices that append it, to the breakdown of the barrier between creative practices and research, and to a whole host of other collapsing dichotomies, and it becomes apparent that ‘neuro-social science’ is only one local effect of a much broader reverberation.

But there’s also a great deal of creativity in this space. It just means we have to consider projects of expertise alongside projects of discipline, mapping out these issues as neither purely matters of expertise nor purely matters of discipline. This is what I hope we’ll manage to explore in my session at the TSR conference on defending the social. It’s a Fireside Chat with Val Gillies and Ros Edwards, as well as their co-author who couldn’t make it when we recorded the podcast below.

In a recent paper, I’ve argued we find a cultural project underpinning ‘big data’: a commitment to reducing human being, in all its embodied affective complexity, stripping it of any reality beyond the behavioural traces which register through digital infrastructure. Underlying method, methodology and theory there is a vision of how human beings are constituted, as well as how they can be influenced. In some cases, this is explicitly argued but it is often simply implicit, lurking beneath the surface of careful choices which nonetheless exceed their own stated criteria.

It’s an argument I’m keen to take further than I have at present and reading Who Cooked Adam Smith’s Dinner by Katrine Marçal  has left me interested in exploring the parallels between homo economicus (and why we are invested in him) and the emerging homo digitalis. Marçal writes on pg 162 of the allure of the former, misunderstood if we see it as nothing more than an implausible theoretical construct or a mechanism to exercise influence over political decision-making:

Many have criticized economic man’s one-dimensional perspective. He lacks depth, emotions, psychology and complexity, we think. He’s a simple, selfish calculator. A caricature. Why do we keep dragging this paper doll around? It’s ridiculous. What does he have to do with us? But his critics are missing something essential. He isn’t like us, but he clearly has emotions, depth, fears and dreams that we can completely identify with. Economic man can’t just be a simple paper doll, a run-of-the-mill psychopath or a random hallucination. Why, if he were, would we be so enchanted? Why would we so desperately try to align every part of existence with his view of the world, even though collected research shows that this model of human behaviour doesn’t cohere with reality? The desperation with which we want to align all parts of our lives with the fantasy says something about who we are. And what we are afraid of. This is what we have a hard time admitting to ourselves. Economic man’s parodically simple behaviour doesn’t mean that he isn’t conjured from deep inner conflicts

What makes homo economicus so compelling? This allure has its roots in a denial of human dependence, describing on pg 155 how our fascination with “his self-sufficiency, his reason and the predictable universe that he inhabits” reflect discomfort with our once having been utterly dependent on others, “at the mercy of their hopes, demands, love, neuroses, traumas, disappointments and unrealized lives”, as well as the inevitability that we will be so again at the other end of the life-course. But he also embodies a vision of what life should be like between the two poles of dependency, as she writes on pg 163:

His identity is said to be completely independent of other people. No man is an island, we say, and think that economic man’s total self-sufficiency is laughable. But then we haven’t understood his nature. You can’t construct a human identity except in relation to others. And whether economic man likes it or not –this applies to him as well. Because competition is central to his nature, his is an identity that is totally dependent on other people. Economic man is very much bound to others. But bound to them in a new way. Bound to them. Downright chained to them. In competition. If economic man doesn’t compete, he is nothing, and to compete he needs other people. He doesn’t live in a world without relationships. He lives in a world where all relationships are reduced to competition. He is aggressive and narcissistic. And he lives in conflict with himself. With nature and with other people. He thinks that conflict is the only thing that creates movement. Movement without risk. This is his life: filled with trials, tribulations and intense longing. He is a man on the run.

If I’m right about the existence of homo digitalis, a clear vision of human constitution underpinning ‘big data’*, we can ask similar questions about this truncated, eviscerated, predictable monad. So complex when we look up close, so simple when we gaze down from on high. Our individuality melts away in the aggregate, leaving us no longer overwhelming but simply overwhelmed. Manageable, knowable, stripped back. Why might this be an appealing vision of human kind? Who might it be appealing to? I’m sure many can guess where I’m going with this, but it’s a topic for another post.

*A term I use to encompass digital social science, commercial and academic, as well as the organisations and infrastructures which it facilitates.

Some tweets about this blog post worry me because it appears as if people think this is my analysis. It’s not. These are my notes on the excellent paper below which I’d strongly recommend reading in full. 

This thought-provoking article by Malcolm Williams, Luke Sloan and Charlotte Brookfield offers a new spin on the familiar problem of the quantitative deficit within U.K. sociology. Many accounts of this sort are concerned with the explanatory implications of this deficit (the phenomena that defy explanation without quantitative terms) while digital sociology is concerned with its implications for computational skills. However, the authors look to a deeper level: the tradition within British sociology which defines itself against quantitative methods. They explore this possibly by drawing a contrast between analytical sociology and critical sociology:

Analytic sociology is the term often used to describe a quite specific version of scientific sociology that combines theories and empirical data to produce sociological explanations (Bunge, 1997; Coleman, 1986; Hedström, 2005; Hedström and Swedberg, 1998). It mostly employs mechanistic explanation and variants on middle range theory. Our use of the term ‘analytic’ encompasses this specific use, but is also broader and meant solely to indicate a sociology that aims to produce descriptions and explanations of social phenomena. It does not exclude ‘understanding’ as methodological virtue, nor does it deny the role of ‘critique’ as an element in the methodological toolkit. It certainly does not exclude qualitative methods and indeed the research described here has qualitative elements

http://journals.sagepub.com/doi/full/10.1177/1360780417734146

Their distinction tracks familiar oppositions between explanation/ understanding and positivism/hermeneutics. Their interest is in how the latter term in each pair was advantaged by the dynamics of expansion in U.K. universities, where (non-quantitative) sociology was a cheap route to expanded student numbers with little to no necessary capital investment. It was during this period of expansion during the 1960s that ‘scientific method’ began to be tied to militarism by the burgeoning anti-war movement. They argue that successive intellectual movements (postmodernism, the linguistic turn, the cultural turn) accentuated this antipathy, such that progressive thought came to be instinctively cautious about quantitative methods. This trend played out within the discipline, its students and teacher, rather than simply being located ‘out there’.

They see this hostility as being dampened by the methodological pluralism encouraged by critical realism and mixed methods pragmatism. But for reasons I don’t understand, which seem to misread the motivations and methods of the critical realist project, incorporate them to analytical sociology:

While there are important differences in the analytic approach (say between realism, post-positivism, and positivism), there is a common core as treating social phenomena as real (or a proxy for real) (Kincaid, 1996) that can be caused, or can cause other social phenomena. The analytic approach shares the common foundations of science: description, explanation, and theory testing and, more specifically, that through the use of appropriate sampling we can generalise from sample to population or from one time or place to another.

http://journals.sagepub.com/doi/full/10.1177/1360780417734146

These are precisely the features which what they call critical sociology rejects as “either methodologically impossible to achieve, in the social world, or ethically undesirable”. More positively, it is concerned with situated meaning and the possibility of emancipation. Their characterisation here is much vaguer but they admit there is an element of strawman to each. Their concern is with how these sociological stereotypes enter into the understanding of students, as extreme versions of actually existing tendencies take hold in the imagination of those who are the next generation of sociologists and the cohorts which the discipline sets loose upon the world.

This is an important possibility because evidence suggests that sociology students are not driven by a fear of number in choosing their degree. Or at least that other mechanisms are at work in bringing about the quantitative deficit within U.K. sociology. The evidence they present suggests a humanistic understanding of sociology is dominant within the student body:

Table 2 clearly shows that the majority of students scored the discipline as closer to the arts/humanities than science/maths. It has been speculated that students taking a prior A-levels in art might be inclined to see sociology as closer to the arts and those taking a mathematics A-Level as closer to science. In fact, though there was some variation at the different measurement points, more students in both groups still thought sociology nearer to the arts/humanities than the sciences.

All but one of subsequent focus groups revealed a “proclivity towards the qualitative involving the theoretical and critique with scepticism about statistics and a clear preference from the students for doing discursive work”. The BSA survey, asking more nuanced questions than the aforementioned survey, produced a more cautious endorsement of sociology’s status:

Table 4 shows that the majority of participants viewed the subject content (64.3%) and status (66.9%) of sociological research as closer to the arts and humanities. In terms of methodology, analytical tools, and public utility, sociology was seen as mid-way between the arts and humanities and the natural sciences

Their overarching argument, supported by intriguing comparative data concerning sociology in Netherlands and New Zealand, concerns how a cultural antipathy to quantitative methods gets reproduced across successive professional cohorts (compounded by the marginalisation of quantitative methods teaching within the broader curriculum):

Many, if not most, sociologists in UK universities have themselves come from a culture of sociology that emphasises critique over analysis, theoretical positions, and qualitative over quantitative methods of enquiry that reflect the historical influences on the discipline, as described above. This culture exists at all levels of teaching, from pre-university A-level teaching through to postgraduate training. Their attitudes and practices incline them ideologically and practically to favour a humanistic and critical attitude towards the discipline, the selection of research questions that require interpretive methods, and often either an expertise in these methods or a preference for theoretical reasoning alone

The result is an absence of methodological pluralism within U.K. sociology, held it seems as a point of principle. They suggest this might also be coupled with a vague sense of persecution, as critical sociology perceives itself as being under threat in a discipline it in fact dominates.

The ensuing ‘split personality’ might be a source of strength for the discipline in troubled times:

In the UK, quite apart from sociology ceding many of its former areas of interest to other disciplines, what sociology is depends on who you ask. The appearance is one of fragmentation. Nevertheless, a counterfactual argument may go something like this: a fragmented discipline might also be described as a diverse one, whose survivability does not depend on the adherence to any particular paradigm. Psychology, for example, which has long been largely associated with experimental method, faces something of a crisis as the statistical reasoning that underpin the experiment have been increasingly challenged in the last two decades (see, for example, Krueger, 2001). Sociology, in the UK, may actually be more agile as a result of its analytic/critique split personality

But crucially there is a risk of the quantitative practitioners exporting themselves from the discipline, even as its capacity to generate them increases:

One might further speculate that those graduate sociologists, from universities with Q-Step centres or other more quantitatively inclined courses, will not necessarily work in sociology or identify as sociologists because they too see it as a primarily humanistic discipline based upon critique, but rather go to other disciplines or become generic ‘social researchers’ with a consequent continuation of the present situation where analytic sociology continues to be a minority pursuit within the UK discipline.

It seems passé to talk about the ‘big data revolution’ in 2017. Much of the initial hype has subsided, leaving us in a different situation to the one in which big data was expected to sweep away all that had come before. Instead, we have the emergence of data science as well as the institutionalisation of computational methods, albeit unevenly, across the full range of the natural and social sciences. Furthermore, addressing the challenge posed by early waves of big data evangelicism to established methodologies, particularly those with a critical and/or hermeneutic focus, has generated a vast outpouring of creativity with the potential to generate significant reorientations within these disciplines. The ‘big data revolution’ has proceeded in a much more constructive way than those early prophets of epochal change were able to predict.

However, we are still far from harmony within the academy. While the intellectual changes driven by big data are well underway, institutional changes of potentially greater importance are still in their infancy. This is how Susan Halford describes the politics of discipline surrounding big data:

How we define Big Data matters because it shapes our understanding of the expertise that is required to engage with it – to extract the value and deliver the promise. Is this the job for mathematicians and statisticians? Computer scientists? Or ‘domain experts’ – economists, sociologists or geographers – as appropriate to the real-world problems at hand? As the Big Data field forms we see the processes of occupational closure at play: who does this field belong to, who has the expertise, the right to practice? This is of observational interest for those of us who research professions, knowledge and the labour market, as we see how claims to expert knowledge are made by competing disciplines. But it is also of broader interest for those of us concerned with the future of Big Data: the outcome will shape the epistemological foundations of the field. Whether or not it is acknowledged, the disciplinary carve-up of big data will have profound consequences for the questions that are asked, the claims that are made and – ultimately – the value that is derived from this ‘new oil’ in the global economy.

https://discoversociety.org/2015/07/30/big-data-and-the-politics-of-discipline/

We can see rapid transformation at this level, with expertise in the social and natural sciences responding to the opportunities and incentives which big data has brought with it. The institutional landscape has begun to change, most notably around funding, with important consequences for how individual and collective agents plan their career-path through this environment. However, this is still unfolding within organisations that have not themselves undergone change as a result of big data. It is this which is likely to change in the coming years. As WonkHe reported earlier this week of the consultation on how the Office for Students will regulate providers of higher education in England:

The consultation will also be looking at the nuts and bolts of the OfS – how will it balance the demands of competition and autonomy while maintaining “proportionate” regulatory approaches? How will the remarkable new powers of entry (extreme audit?) be used? What sanctions will be available to the new regulator, and how will they be applied? Following strong ministerial direction, we can also expect measures on senior staff pay to feature prominently, but what form will they take, and will they have any real teeth? And how will approaches compare to other sectors?

Widely expected is an end to regular institutional visits – the “periodic review” is likely to be replaced by a new method for the OfS to use live data to monitor institutions. It may well be easier than the annual submission, but now is a good time to be a big data wonk, as new systems and process will need to be established in institutions to respond to a new approach.

This concern for real time metrics, institutionalising transactional data into the fabric of higher education itself, only seems likely to grow. What does this mean for the politics of discipline? My hunch is that the big data revolution within higher education has only just begun and that it’s eventual form will be different to that which most predicted.

I just came across this remarkable estimate in an Economist feature on surveillance. I knew digitalisation made surveillance cheaper but I didn’t realise quite how much cheaper. How much of the creeping authoritarianism which characterises the contemporary national security apparatus in the UK and US is driven by a familiar impulse towards efficiency?

The agencies not only do more, they also spend less. According to Mr Schneier, to deploy agents on a tail costs $175,000 a month because it takes a lot of manpower. To put a GPS receiver in someone’s car takes $150 a month. But to tag a target’s mobile phone, with the help of a phone company, costs only $30 a month. And whereas paper records soon become unmanageable, electronic storage is so cheap that the agencies can afford to hang on to a lot of data that may one day come in useful.

http://www.economist.com/news/special-report/21709773-who-benefiting-more-cyberisation-intelligence-spooks-or-their

In reality, it is of course anything but, instead heralding a potentially open ended project to capture the world and achieve the utopia of total social legibility. An ambition which always makes me think of this short story:

The story deals with the development of universe-scale computers called Multivacs and their relationships with humanity through the courses of seven historic settings, beginning in 2061. In each of the first six scenes a different character presents the computer with the same question; namely, how the threat to human existence posed by the heat death of the universe can be averted. The question was: “How can the net amount of entropy of the universe be massively decreased?” This is equivalent to asking: “Can the workings of the second law of thermodynamics (used in the story as the increase of the entropy of the universe) be reversed?” Multivac’s only response after much “thinking” is: “INSUFFICIENT DATA FOR MEANINGFUL ANSWER.”

The story jumps forward in time into later eras of human and scientific development. In each of these eras someone decides to ask the ultimate “last question” regarding the reversal and decrease of entropy. Each time, in each new era, Multivac’s descendant is asked this question, and finds itself unable to solve the problem. Each time all it can answer is an (increasingly sophisticated, linguistically): “THERE IS AS YET INSUFFICIENT DATA FOR A MEANINGFUL ANSWER.”

In the last scene, the god-like descendant of humanity (the unified mental process of over a trillion, trillion, trillion humans that have spread throughout the universe) watches the stars flicker out, one by one, as matter and energy ends, and with it, space and time. Humanity asks AC, Multivac’s ultimate descendant, which exists in hyperspace beyond the bounds of gravity or time, the entropy question one last time, before the last of humanity merges with AC and disappears. AC is still unable to answer, but continues to ponder the question even after space and time cease to exist. Eventually AC discovers the answer, but has nobody to report it to; the universe is already dead. It therefore decides to answer by demonstration. The story ends with AC’s pronouncement,

And AC said: “LET THERE BE LIGHT!” And there was light

https://en.wikipedia.org/wiki/The_Last_Question

From Douglas Rushkoff’s Throwing Rocks at the Google Bus, loc 2256:

Besides, consumer research is all about winning some portion of a fixed number of purchases. It doesn’t create more consumption. If anything, technological solutions tend to make markets smaller and less likely to spawn associated industries in shipping, resource management, and labor services.

Digital advertising might ultimately capture the entirety of advertising budgets, but it does nothing to expand these budgets. There are upper limits on the revenue growth of the corporations that define the ‘attention economy’: how are they going to respond to these?

I’m very interested in this concept, which I was introduced to through the work of Pierpaolo Donati and Andrea Maccarini earlier this year. It emerged from the work of Arnold Gehlen and refers to the role of human institutions in unburdening us from existential demands. This is quoted from his Human Beings and Institutions on pg 257 of Social Theory: Twenty Introductory Lectures by Hans Joas and Wolfgang Knobl. He writes that institutions

are those entities which enable a being, a being at risk, unstable and affectively overburdened by nature, to put up with his fellows and wit himself, something on the basis of which one can count on and rely on oneself and others. On the one hand, human objectives are jointly tackled and pursued within these institutions; on the other, people gear themselves toward definitive certainties of doing and to doing with in them, with the extraordinary benefit that their inner life is stabilized, so that they do not have to deal with profound emotional issues or make fundamental decisions at every turn.

In an interesting essay last year, Will Davies reflected on the ‘pleasure of dependence’ in a way which captures my understanding of entlastung. It can be a relief to trust in something outside of ourselves, settling into dependence on the understanding that our context is defined by a degree of reliability due to an agency other than our own:

I have a memory from childhood, a happy memory — one of complete trust and comfort. It’s dark, and I’m kneeling in the tiny floor area of the back seat of a car, resting my head on the seat. I’m perhaps six years old. I look upward to the window, through which I can see streetlights and buildings rushing by in a foreign town whose name and location I’m completely unaware of. In the front seats sit my parents, and in front of them, the warm yellow and red glow of the dashboard, with my dad at the steering wheel.

Contrary to the sentiment of so many ads and products, this memory reminds me that dependence can be a source of deep, almost visceral pleasure: to know nothing of where one is going, to have no responsibility for how one gets there or the risks involved. I must have knelt on the floor of the car backward to further increase that feeling of powerlessness as I stared up at the passing lights.

http://thenewinquiry.com/essays/the-data-sublime/

At a time when entlastung is failing, when institutions are coming to lose this capacity to unburden us, could faith in self-tracking, big data and digital technology fill the gap? The technological system as a whole comes to constitute the remaining possibility of entlastung and we enthusiastically throw ourselves into its embrace, as the only way left to feel some relief from the creeping anxiety that characterises daily life.

The essay by Will Davies is really worth reading: http://thenewinquiry.com/essays/the-data-sublime/

From Infoglut, by Mark Andrejevic, loc 607. The context to digital innovation in public services: 

What emerges is a kind of actuarial model of crime: one that lends itself to aggregate considerations regarding how best to allocate resources under conditions of scarcity – a set of concerns that fits neatly with the conjunction of generalized threat and the constriction of public- sector funding. The algorithm promises not simply to capitalize on new information technology and the data it generates, but simultaneously to address reductions in public resources. The challenges posed by reduced manpower can be countered (allegedly) by more information. As in other realms, enhanced information processing promises to make the business of policing and security more efficient and effective. However, it does so according to new surveillance imperatives, including the guidance of targeted surveillance by comprehensive monitoring, the privileging of prediction over explanation (or causality), and new forms of informational asymmetry. The data- driven promise of prediction, in other words, relies upon significant shifts in cultures and practices of information collection.

From InfoGlut, by Mark Andrejevic, loc 464:

The dystopian version of information glut anticipates a world in which control over the tremendous amount of information generated by interactive devices is concentrated in the hands of the few who use it to sort, manage, and manipulate. Those without access to the database are left with the “poor person’s” strategies for cutting through the clutter: gut instinct, affective response, and “thin- slicing” (making a snap decision based on a tiny fraction of the evidence). The asymmetric strategies for using data highlight an all- too- often overlooked truth of the digital era: infrastructure matters. Behind the airy rhetoric of “the cloud,” the factories of the big data era are sprouting up across the landscape: huge server farms that consume as much energy as a small city. Here is where data is put to work – generating correlations and patterns, shaping decisions and sorting people into categories for marketers, employers, intelligence agencies, healthcare providers, financial institutions, the police, and so on. Herein resides an important dimension of the knowledge asymmetry of the big data era – the divide between those who generate the data and those who put it to use by turning it back upon the population. This divide is, at least in part, an infrastructural one shaped by ownership and control of the material resources for data storage and mining. But it is also an epistemological one –a difference in the forms of practical knowledge available to those with access to the database, in the way they think about and use information.

I’d been planning to read his work for a while but I’m finding it almost eery how relevant it is. This is exactly what I was trying to argue in my forthcoming chapter on Fragile Movements but Andrejevic expresses it much more effectively than I was able to. The project as a whole is about the sociology of group formation under these conditions, as well as how this contributes to the continuing development of digital capitalism.

More on this from Infoglut loc 870:

In this regard the digital era opens up a new form of digital divide: that between those with access to the databases and those without. For those with access, the way in which data is understood and used will be fundamentally transformed. There will be no attempt to read and comprehend all of the available data – the task would be all but impossible. Correlations can be unearthed and acted upon, but only by those with access to the database and the processing power. Two different information cultures will come to exist side by side: on the one hand, the familiar, “old- fashioned” one in which people attempt to make sense of the world based on the information they can access: news reports, blog posts, the words of others and the evidence of their own experience. On the other hand, computers equipped with algorithms that can “teach” themselves will advance the instrumental pragmatics of the database: the ability to use tremendous amounts of data without understanding it.

From InfoGlut, by Mark Andrejevic, loc 601:

The fictional portrayals envision a contradictory world in which individual actions can be predicted with certainty and effectively thwarted. They weave oracular fantasies about perfect foresight. Predictive analytics, by contrast, posits a world in which probabilities can be measured and resources allocated accordingly. Because forecasts are probabilistic, they never attain the type of certitude that would, for example, justify arresting someone for a crime he or she has not yet committed. Rather, they distribute probabilities across populations and scenarios.

The most pressing question this raises for me concerns the micro-sociology of algorithmic authority. To what extent are the algorithms black boxed by those ‘on the ground’? Does awareness of the probabilistic character of the forecast drop out of the picture in the social situations in which actors are intervening on the basis of these forecasts? How much implicit authority derives from the fact ‘the algorithm says so’, even if those designing the underlying system would stress that the forecasts are probabilistic? How does this vary between different groups? It’s easy to imagine securely embedded professionals (e.g. Doctors) treating these forecasts with care, not least of all because many already do so as a routine part of working life, but what if algorithmic authority is a corollary to deliberate deskilling? What if interventions are made by those who are precariously employed? As much as I dislike the show in question, it’s hard not to think of this when reflecting on these issues:


These are empirical questions which are hugely important for the study of algorithmic authority. I’m planning to start looking for ethnographic and ethnomethodological literature which can shed light on them, even if not directly addressed to questions of algorithms. Any reading suggestions are, as ever, much appreciated.