The decolonial turn is on the road to contingency

ABSTRACT

In this short commentary, I reflect on the situated perspective that grounds data colonialism as a critique of the nature of control embedded in contemporary data-driven practices. I argue that data-driven practices represent particular configurations of control and contingency. While the data colonialism thesis is a consequential orienting principle to analyze control, the pursuit of the decolonial turn in critical data and technology studies also requires contending with the contingencies of designing, implementing, and appropriating data-driven practices.

KEYWORDS:

• Data colonialism
• Decoloniality
• Big Data
• Contingency

View addendum:

Data colonialism: compelling and useful, but whither epistemes?

The decolonial turn in data and technology research: what is at stake and where is it heading?

Response

If the data colonialism thesis (Couldry & Mejias, 2018, 2019) were a building, its foundation would be laid on two core arguments. First, data is an asset owned by the people who produce it. Production and ownership of data are inextricably linked, and we should not speak of one without the other. Second, the current infrastructural conditions have been insidiously designed by digital service providers to extract, circulate, and interpret this data as capital often without the informed consent or knowledge of their users who produce/own it. The lesser the agency of users in shaping their data, the more oppressive is their relationship with digital service providers. These two arguments taken together illustrate an emergent form of historical landgrab colonialism within contemporary data capitalism. Furthermore, they change the nature and meaning of colonialism itself as it becomes grounded in lived experiences of ‘dispossession through data’ (Couldry & Mejias, this issue) as data-driven practices proliferate in all aspects of everyday life.

The thesis has an intuitive appeal; after all, only a handful of giant private corporations account for most of the traffic on the internet. The patterns in the growth of the data economy in the last few decades shows how attention is commodified (Davenport & Beck, 2001) as an untapped resource of/for user data. These corporations have colonized this resource and extracted immense capital from it. In this short commentary, I reflect on the situated perspective that grounds data colonialism as a particular form of critique of the nature of contemporary data-driven practices. I argue that while the data colonialism thesis is a consequential orienting principle, the pursuit of the decolonial turn in critical data and technology studies requires contending with the contingencies of designing, implementing, and appropriating data-driven practices.

Engaging with the reflections of Couldry and Meijas (this issue) on the data colonialism thesis reminded me of a conversation I had with an entrepreneur in Bengaluru, India while conducting fieldwork for my dissertation research on Aadhaar (Singh & Jackson, 2021), India’s biometrics-based national identification infrastructure. Aadhaar uses biometric information of Indian residents to assign a unique 12-digit identification number to them (UIDAI, 2010). This uniqueness of data is the harbinger of a plethora of data-driven services in the Indian economy. The entrepreneur wanted to build on Aadhaar’s identity verification services to develop a LinkedIn-style mobile app for hiring and establishing creditworthiness of blue-collar workers. He wanted to build trust profiles and trust scores for the users of his app. He narrated a story to justify the need for such trust profiles in India:

In India, there are a lot of women that sell fish on the roadside. They have no money. So, when fish comes, they borrow money from a money lender, say ₹1000 [∼$13.50]. With that ₹1000, they buy some number of fish. They hope to sell that fish during the day. At the end of the day, they must repay ₹1200 [∼$16], so your daily interest is 20%. And outside the ₹1200, whatever they make, they keep. This is a cycle that goes, day in, day out.

Now, if I have to replace that money lender, I have to have a system which is as frictionless as that moneylender. If I can go there, and say, ‘Hey listen! You authenticate yourself [using Aadhaar] in a second. So, I know you’re XYZ. I will dispense you ₹1000 right here. At the end of the day, you authenticate yourself again. … And I am going to roll back in, maybe ₹1050 [$14]. Instead of charging you 20%, I am going to be charging you 5%’.

So, now what happens at the backend, if I am doing this day in and day out with this lady for eight months, I have collected 240 days of data points on her. Now, if she goes to a bank and says, ‘Hey listen! I cannot read and write, but here is all the information about me that you can look up at [Trust Score Company] that they have collected. Now I am aspirational. I don’t want to do this daily business. I want to set up a small stall. Can you give me a loan?’ In today’s context, the bank would say, ‘I don’t even know who you are, please don’t even enter my premises’. [… But, in case of this woman,] the bank can at least have a conversation that, ‘Okay, at least we have some information about this individual’. How else would you do financial inclusion? (Interview with an entrepreneur, 24 July 2016).

There are multiple ways to make sense of this story. For the entrepreneur, this story is a call for disruption in oppressive practices of charging high interest rates on small daily loans. His app not only breaks this cycle of oppression by charging a smaller interest rate, but also provides an opportunity to its users to represent their creditworthiness in India’s formal economy. For the women, the app is at least an alternative to their existing arrangement with the money lender. However, without their voice to shape it, the story is agnostic to local culture, politics, and power arrangements of the fish market. For example, if the money lender controls this market, then even if this app for easy access to loans works, it might not be used. As users of the app, these women also need to navigate the requirement of ‘data infrastructure literacy’ (Gray et al., 2018) to understand the nature of their transactions with the app, exercise control over their data, and secure due process when things go wrong. For advocates of inclusion and equity, this story offers a point of entry into the contradictions in the contingencies of organizing data-driven services. Often the absence of data is offered as an explanation for the limits in people’s ability to leverage data-driven services. At the same time, the presence of data is not necessarily a resource for empowerment. Even when forms of data are available, people who contend with uneven structural inequities along well-recognized intersections of gender, race, class, and caste must also cope with emergent conditions of participation, marginality, and bias while accessing data-driven services. For analysts who engage with data colonialism as their frame of analysis, this app must, over time, take away control of the app’s users over their data. One way in which this loss of control may manifest is when the entrepreneur approaches the bank, instead of the women, with the purpose of selling the data extracted from these women over time as data about ‘trustworthy users’ who could be targeted for banking services. Such a future is plausible, but not a given.

In illustrating different approaches to unpack this story, my effort here is to showcase how different discourses of disruption, alternatives, contingency, and control impute different meanings to the function of the app that scores its users for ‘trustworthiness.’ The data colonialism thesis draws its roots from the discourse of control, wherein data-driven practices are intentionally designed to be extractive and exploitative. It resolves the challenge of decolonizing the experiences of living with these practices by again intentionally building towards a discourse of alternatives in conceptualizing the Non-Aligned Technologies Movement (NATM), rooted in boycotting Big Tech and actively looking for non-extractive digital technologies. What remains unaccounted in the data colonialism thesis is the gap between intention and action. For example, there are poignant historical accounts of this gap which show how high modernist design of state interventions for controlling its citizenry have failed (Scott, 1998). This gap is also reminiscent of the gap between plans and situated actions (Suchman, 2007). While plans (and intentions that ground them) present a normative configuration of people and things to accomplish a given task (and a vision for control), situated actions bring to light the contingent and contextual reconfiguration(s) of these entities to practically accomplish them. Data colonialism offers an insightful analysis of the often-implicit intentions embedded in the infrastructural conditions perpetuated through data-driven practices. Building on it, we also need ongoing investments in reflecting on and engaging with the multi-faceted contexts that make up data-driven practices that sustain as well as disrupt existing ways of life.

An example to consider here is that while data systems are often designed to serve one purpose, they are increasingly repurposed to resolve other social problems and achieve emergent visions for the future. This repurposing not only embeds data systems more deeply into everyday life, but it also changes the meaning of the data collected from its users. In the context of the entrepreneur’s story, a woman using her own data to represent/claim her creditworthiness is not the same as the entrepreneur selling data of ‘trustworthy users’ of his app to third parties. The app, however, affords the conditions for both these possibilities. On some occasions, the gap between intention and action is clear and straightforward, on other occasions such as when both these possibilities happen simultaneously, it becomes difficult to demarcate between intention and action. How these possibilities manifest is a contingent accomplishment of diverse sociocultural, technical, regulatory, legal, and market interactions.

Data-driven practices represent particular configurations of control and contingency. They work seamlessly for some at the expense of others. However, only rarely and perhaps in the most trivial senses can we show the concomitant relationship between the colonial impulse and the design intentions that underlie data systems. There are always alternative explanations that can be brought to bear upon the experiences of living with them. With data colonialism thesis as an intuitive place to begin the pursuit of the decolonial turn, it is equally important to move towards attending to the unique contingencies of different data-driven practices from around the world that make control possible in the first place. After all, data-driven practices are similar, but not the same.

Acknowledgments

I thank Rigoberto Lara Guzmán, Malte Ziewitz, and Rajesh Veeraraghavan for conversations and feedback on this piece. Field research mentioned in this commentary was supported by the NSF Doctoral Dissertation Research Grant #1655753.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Division of Social and Economic Sciences: [Grant Number 1655753].

Notes on contributors

Ranjit Singh

Ranjit Singh is a Postdoctoral Scholar at the AI on the Ground Initiative of Data & Society Research Institute and has a doctorate in Science and Technology Studies from Cornell University. He studies the intersection of data infrastructures, global development, and public policy. He is currently leading a research project on and collaborating on building a research community around mapping the concepts, keywords, and everyday stories centered on data-driven interventions and AI in/from the Global South.