Yesterday morning, I was at the Canadian Open Data Summit (CODS), where a lot of the discussion was around the benefits of open data for governance. Beth Blauer from Johns Hopkins discussed her experience working in various muncipalities in developing data infrastructure to support governance decisions. This led to the following tweet from the Edmonton Mayor’s Office:
"If we want to solve problems like healthcare or climate change, we need to do it from a basis of evidence. Open Data provides that" #CODS17 pic.twitter.com/eVL5B517TW— Edm Mayor Office (@YEGMayorOffice) June 13, 2017
The presumption here that open data automatically provides the evidence required to solve socio-political problems is, I think, too simplistic. It relies on a determinism just as dangerous as technological determinism, in that it removes the human relationships (i.e. the politics) from the questions of how the data is collected in the first place and by whom. How are data elements defined? What is considered non-data, with respect to the simplification of the empirical world to the data model? These are all political questions, implicated in dynamics of socio-economic power and domination. Ignoring the political questions around the creation of data makes it easier, I think, to ignore the political in the question of data use. Beth Blauer talked about “the intersection of personal and public data”, essentially talking about behavioural control based on mining personal data (search history, calendars, medical records, school enrollment) with public data (neighbourhood information, school information, weather data, etc). The uncritical acceptance of the neutrality and truth of open data means that we trust this intersection in the same way that we trust the (filter-bubbled) Google results that are presented to us.
This feeds in to some thoughts I’ve been having about technology as the mediator of the transition from the society of discipline (Foucault) to the society of control (Deleuze). We can see in the recent US and UK elections how false or spun data can manipulate (control) populations. One effect of removing a visible human presence from the mediating of technology is that a) data and algorithms become obscured and more likely to embody “algorithms of oppression” (Safiya Noble’s term) and b) technology is seen as both neutral and true when the human determinations are made invisible. We like to think that a technological solution is unbiased, and data driven technologies are considered to be empirically sound and politically unmotivated. And data determinism - like technological determinism - makes data the active driver of behaviour and decision-making, by obscuring the political conditions of its creation.
There’s also a rhetorical component to this. As with classic statements of technological determinism (Ursula Franklin’s for example), data determinism puts data as the subject in sentences. So when Dark Horse Analytics tweets:
Discussing ways #OpenData transforms society through innovation at @OpenDataEdm this week >> https://t.co/gjxFGjlvhH #CODS17 pic.twitter.com/qcvn8v5yB1— Darkhorse Analytics (@dhanalytics) June 12, 2017
In this view, it is “Open Data” that “transforms” society, rather than the agency and decision making of people. Data, like technology, can be a tool in the transformation of society, but only people can do it. The rhetorical strategy of making data the subject of sentence, makes data seem like the subject of the acts themselves. From there, the invisibility of human, political dynamics plays out as above. This is a classic case of what Marxists call fetishism or reification, the method by which relations between people appear as relations between people and things (and often simply between things). In this way the social relations of capitalis, which are relations of domination, exploitation, and oppression, are hidden from view.
When I tweeted this, during Beth Blauer’s talk:
Also an implicit assumption that open data is both neutral and true... https://t.co/otPaTiRPM6— redlibrarian (@redlibrarian) June 13, 2017
My friend @chefquix asked what was wrong with data-driven decision-making. My argument is that if we presume data to be objective, we ignore the decision making that went into the construction and collection of the data in the first place. Just as we have begun to think critically about the social relationships and biases that go into algorithms (something else engineers would like to think of as objective), we need to recognize that the same mechanisms of oppression and domination that are encoded in algorithms are encoded in the ways we design our data collection. “Data” is not a natural phenomenon, conveniently recorded, it is just as socially constructed as anything else, and represents a model, that is a simplification, of the empirical world that has been decided a priori (and often unconsciously).
A perfect example of what I’m talking about came up at the afternoon panel on “Open Data and First Nations in an Era of Reconciliation”, which included three women - Leona Star, Mindy Denny, and Bonnie Healy  - involved in indigenous health initatives in Manitoba, Alberta, and Nova Scotiat, and how assessment and measurement in this area relate to sovereignty in general and data sovereignty in particular. They argued persuasively about the ways in which cultural, racial, and socio-economic prejudices (in the strict sense of judgements that come before the collection of data) condition and constrain the ways in which data are collected and phenomena are described. In this case, the connection between the political decisions that go into constructing data and the very real social, economic, and political effects is starkly apparent. By presuming that “data” is an objective reflection of the empirical facts, we end up supporting and reinforcing the worldviews, biases and prejudices that create that world in the first place. It is in this sense that “data-driven” can easily elide into “data determinism”, in which the data that we believe to be objective and value-neutral in fact props up the very world views we are hoping to investigate. What began as a spurious, rhetorical data determinism becomes a very real determinism, one which undermines many of the social justice initiatives that are a core part of the open data community.
 I honestly can’t wait for Safiya Noble’s Algorithms of Oppression book, which looks like won’t be out until next spring sometime.
 Interestingly, their names don’t appear in the conference programme or on the CODS website.