My notes on Caplan, R., & Boyd, D. (2018). Isomorphism through algorithms: Institutional dependencies in the case of Facebook. Big Data & Society, 5(1), 2053951718757253.

Are data-driven technologies leading organisations to take on shared characteristics? This is the fascinating question addressed in this paper by Robyn Caplan and danah boyd which they begin with the example of news media. The popularity of social media platform as intermediaries has forced many news media producers to change their operations, increasingly producing with a view to popularity on these platforms. As they put it, “these platforms have upended the organizational practices of news-producing platforms, altering how both the newsroom and individual journalists operate” (2). They use the concept of isomorphism to understand how “algorithms structure disparate businesses and aims into an organizational field, leading them to change their goals and adopt new practices” (2). This is a process of homogenisation, as organisations reconstruct themselves into a field orientated around the assumptions embedded into the t mediating platform. The ensuing ambiguity has regulatory consequences, as social media platforms are not straight forward media actors but nor are they mere intermediaries. By theorising algorithmic mediation as akin to bureaucratisation, it become easier to identify the precise character of the role of platforms within it. It also makes clear the continuities with earlier isomorphic processes, for instance as corporate software platforms introduced common features to organisations.

The roots of this connection are deep. They argue that “algorithms that serve to pre- process, categorize, and classify individuals and organizations should be viewed as extensions of bureaucratic tools such as forms, that have been associated with the state in the past” (3). Software like Lotus 1-2-3 and Microsoft Office restructured business activity through the affordances it offered to digitalise bureaucratic processes and algorithmic technologies should be seen as a further extension of this process. The neutrality which animated the promise of bureaucracy is also often expressed in the belief that algorithmic judgement will negate the role of subjectivity and bias in decision making processes. This is obscured by the familiar black box of the algorithm but also the mythology of its uniqueness, seeing it as something distinct from previous organisational processes. However if we see algorithms as organisational phenomena then the problem comes to look quite different, simultaneously more straight forward but also more challenging because the problems will likely spiral outwards across dependent organisations. 

They use DiMaggio and Powell’s concept of isomorphism which considers how a common environment can lead otherwise different units of a population facing that environment to come to resemble one another. For organisations this occurs through one organisation becoming dependent on another organisation, with the expected degree of resemblance tracking the degree of that dependence. For instance in the case of Facebook’s newsfeed, the concept of what is ‘relevant’ has been redefined by the vast size of the audience whose access is mediated through this mechanism. The dependence of the news media on that mechanism means they come to reproduce its characteristics, increasingly operating with a view towards metrics like clicks, likes and shares. The early winners in the Facebook ecosystem were those publishers like Buzzfeed and Upworthy who “subsumed their own organizational practices to the logic of Facebook’s algorithms” (5). But Facebook’s attempts to modulate this mechanism in order to produce what they deemed better quality results inevitably leads the actors dependent upon it to make adaptive changes in response to these modulations. Mimesis thrives in this environment as they explain on pg 6-7:

“Changes stemming from coercive forces, especially when frequent, lead to an environment of uncertainty that prompts dependent organizations to learn from other dependent organizations that have successfully conformed to the structuring mechanisms. This process of ‘‘mimesis,’’ or imitating models for success, is another process DiMaggio and Powell (1983: 151) argue will induce similarity across an organizational field. In this sense, the dominant organization’s incentives or goals become embedded across an industry through the borrowing of practices that lead to success over the network. In the case of Facebook, this was seen in the adoption of data-driven metrics and analytics into newsrooms, as well as the growth of a new set of intermediaries that were fed directly by the Facebook API, whose role it was to analyze and com- municate Facebook metrics back to publishers”

A further ecosystem of intermediaries thrives under these circumstances, as new players emerge who help the firms concerned address their common problems. These responses to uncertainty are driven by a concern to “demonstrate to others that they are working to change their practices to be in-line with those of the dominant organization“ (7) as well as increasing possibilities for success. The discussion of professionalisation is really important for my interests. The roles themselves changed as a result of isomorphism, with normative pressure to enact new functions and perform new skills which contrbute to the success of the organisation. This is my concern about the institutionalisation of social media within higher education. There’s a lot here which I’m going to need to go back to and I think it’s crucial for my developing project on the digital university. 

This insightful article paints a worrying picture of the growth of data-driven policing. The technical challenge of “building nuance” into data systems “is far harder than it seems” and has important practical implications for how interventions operate on the basis of digital data. What I hadn’t previously realised was how readily investigators are using social media on their own initiative above and beyond the systems that are being put into place with the help of outside consultancies: only 9% of police using social media in investigations had received training from their agency. Furthermore the discussion of the life span of data raised some really interesting (and worrying) questions about the organisational sociology of data-driven policing given what seems likely to be increasing involvement of the private sector in policing in the UK:

For the kid listed in a gang database, it can be unclear how to get out of it. In the world of human interaction, we accept change through behavior: the addict can redeem himself by getting clean, or the habitual interrupter can redeem himself by not interrupting. We accept behavior change. But in the database world, unless someone has permission to delete or amend a database record, no such change is possible. Credit agencies are required to forgive financial sins after 7 years. Police are not—at least, not consistently. The National Gang Center, in its list of gang-related legislation, shows only 12 states with policies that specifically address gang databases. Most deny the public access to the information in these databases. Only a few of these twelve mention regular purging of information, and some specifically say that a person cannot even find out if they have a record in the database.

This permanence does not necessarily match real-world conditions. Kids cycle in and out of street gangs the way they cycle in and out of any other social group, and many young men age out of violent behavior. Regularly purging the gang database, perhaps on a one-year or two-year cycle, would allow some measure of computational forgiveness. However, few institutions are good at keeping the data in their databases up-to-date. (If you’ve ever been served an ad for a product you just bought, you’re familiar with this problem of information persistence and the clumsiness of predictive algorithms.) The police are no worse and no better than the rest of us. Criminologist Charles Katz found that despite a written department policy in one large Midwestern police gang unit, data was not regularly audited or purged. “The last time that the gang unit purged its files, however, was in 1993—approximately 4 years before this study was conducted,” he wrote. “One clerk who is responsible for data entry and dissemination estimated, ‘At a minimum, 400 to 500 gang members would be deleted off the gang list today if we went through the files.’ Accordingly, Junction City’s gang list of 2,086 gang members was inflated by approximately 20% to 25%.”

This suggests to me that any adequate evaluation of data-driven policing needs to take questions of organisational sociology and information technology extremely seriously. What matters is not just the formulation of data management policies but what we know about how such policies tend to be implemented under the specific conditions likely to obtain in policing. Given the broader trend towards the privatisation of policing, it is increasingly important that we understand how sharing of data operates across organisational boundaries, how it is prepared and how it is perceived by end-users.

My fear is that a form of inter-organisational ‘black-boxing’ could kick in where those utilising the data for interventions trust that others have elsewhere taken responsibility for ensuring its reliability. What scrutiny would the operations of outside suppliers be subject to? Could privatisation intensify the rush towards data-driven policing in the name of efficiency savings? Would a corresponding centralisation of back-office functions compound the aforementioned epistemological risks entailed by outsourcing? These are all urgent questions which could easily be marginalised as budgetary constraint drives ‘innovation’ in policing: data-driven policing and privatised policing will likely go hand-in-hand and we need to analyse them as such.

I wrote yesterday about how obsessive auditing produces a profession which is incompatible with a normal life. Two interesting comments offered really important insights into this issue:

  1. “let experts come in and help you” – that’s the motivation, the creation of a massive industry of assessors, advisors and expensive literacy and numeracy schemes. Some people have got very rich from this.

  2. Performance monitoring is a technology. Its main thrust is to effect a ‘re-attribution of responsibility’ from those deploying the technology to those who become its objects. The main difference between schools and universities is that the technology is aimed at whole schools, so, e.g. so one can talk about ‘failing managers’. In universities it is the managers that deploy the technology. After all, it cannot possibly be the managers that fail when research rankings drop, NSS scores sink or student recruitment falters. I once worked briefly in an institution where academic staff ftes were linked to student fte recruitment. Academic staff ‘recruitment performances’ were monitored. If student recruitment fell then academic staff were laid off. Meanwhile the same institution had a growing ‘ Marketing and Recruitment’ department. This department was unaffected by any drop in recruitment – as a management department its ‘performance’ was not monitored. There was no monitoring technology to do this. Oddly, whenever the work involved in recruitment arose (producing copy for brochures, marketing in schools, dealing with ad hoc inquiries etc.) these were all directed to the academics on the grounds that they ‘best knew their own programmes’ etc. Staff in Marketing had standard non-academic appraisals so there was no performance criteria critical to the institution’s strategic aims in their personal record. The responsibility for all critical criteria are always transferred to staff who are positioned in such a way as to be least able to affect the context of those criteria.

    School teachers are simply in an absurd situation. Frankly one cannot have a good conversation while the interlocutors are focussed on a screen monitoring the metadata of that conversation.

What both point to is the importance of vested interests. My reluctance to understanding this process as governmentality is that it easily slides into a mystification of elites. The conceptual vocabulary utilised here tends to construct these outcomes as the operations of diffuse power rather than specific projects undertaken by those with vested interests in their outcomes: management departments, communications departments or consultancies etc. I’ve often wondered about what performance management regimes those working in university communications departments are subject to given how much of their output seems to be of questionable quality.

Incidentally, this is why I have such a problem with the emerging industry of ex-academics coaching graduate students. On the one hand, it could be seen as no different to private tuition, something which reproduces inequality through a market transaction. On the other hand, it could be seen as a direct interest in the processes of heating up the floor to see which graduate student can keep hopping the longest through contributing to the ratcheting up of the expectations inherent in the role of ‘graduate student’ and a tendency to talk up the problems confronting graduate students as a whole.

In this very useful paper Dave Elder-Vass observes that the concept of ‘social institution’ is almost as diverse as that of ‘social structure’:

The concept of social institution is almost as diverse in its referents as the concept of social structure. The Collins Dictionary of Sociology, for example, begins its definition: ‘an established order comprising rule-bound and standardized behaviour patterns. The term is widely acknowledged to be used in a variety of ways, and hence often ambiguously. Social institution refers to arrangements involving large numbers of people whose behaviour is guided by norms and roles’ (Jary and Jary 2000: 302).

He identifies a number of different ways in which institutions have been conceptualised:

  1. Regular patterns of behaviour
  2. The normative beliefs held by individuals which account for these regularities (individual representations)
  3. The normative beliefs held by collectives which account for these regularities (collective representations)
  4. The ‘virtual’ systems of rules and resources that are instantiated in individual practices (structuration)