Deconstructing Surveillance Week 7 — Imagination, Hope, Liberation
As a reminder, this is part of a series of write-ups based off a class I’m teaching at Tufts University called Data and Power: Deconstructing Surveillance. If you would like more of a description of what the class is, check out the post from Week 1. If you want to keep up with this series, you can subscribe to receive email updates or on Medium where the write-ups are posted!
This week starts us on our journey towards understanding how to make change given the systems we learned to analyze in the past 6 weeks. Making change is no easy task. Often, it seems so impossible that we altogether give up, or shudder to even consider starting. But after grappling with the consequences of surveillance systems, especially some of the most unjust consequences, we cannot help but ask “how can these change?”
To prepare us for this journey into change, I thought it would be important to first address one of the hardest parts of change: feeling that it is possible. Towards this end, we watched talks from two speakers: Ruha Benjamin and Angela Davis. Both are incredible thinkers, advocates, and action-oriented practitioners who constantly strive to change conditions of injustice. A theme that they both discuss is the need to imagine — conjuring a different world, where the systems we disagree with are not present, so that we can shift current conditions. To this end, we flex and build our imaginative capacities.
Once we are somewhat more equipped to imagine new and better worlds, it will be helpful to understand along which lines data systems can be reimagined so that they strike at the core of what we find untenable about them. We are helped on this journey by Catherine D’Ignazio and Lauren Klein, whose fantastic book, Data Feminism, can lend some insight. The first two chapters introduce us to concepts that can help: the matrix of domination, collective liberation, and plenty of examples of data science projects that challenge power.
Imagine & Hope
“This is not the way things are supposed to be. This might be the way they are now, but they are not supposed to be this way. They will not always be this way.” Angela Davis reminisces on these words from her mother, speaking to her about racism and segregation in the U.S. when she was a girl. A world so backwards and hateful had to be changed, but to do so, a different world had to be imagined.
Davis emphasizes how important it is, in her view, to adopt habits of perception and imagination. When faced with oppression, thinking beyond the moment is a critical step towards change, when combined with collective intervention.
Her life has been spent pushing for changes — particularly those that are not popular with those who occupy positions of power. From racial desegregation to the rights of women, Davis has had a life of activism, organizing, and disrupting the status quo. Her academic work, pushing forward the theories and tenets of critical theory, especially applied to black and feminist liberation, has been no different. Davis is no stranger to making change, as her life has undoubtedly helped lead to a world that is far different than the one she grew up in.
Ruha Benjamin echoes a similar prescription, connecting imagination with changing the technological status quo by urging listeners to “recuperate [their] own agency vis-a-vis technology.” She similarly describes technological oppression as values embedded into technological systems, where the path to change requires fighting a battle on the level of imagination. “Most people are forced to live in someone else’s imagination,” she argues. Our job is to move beyond technology that dominates, and instead make it reflect our highest values.
Benjamin, as a black female sociologist bringing the ideas of African American studies to the white male dominated realm of digital technology, is also no stranger to pushing for what some consider “radical” change. Her vision has been crucial in pushing data science ethics forward, forcing it to grapple with its anti-black racial dimensions.
Both Davis and Benjamin make the critical point that to begin to make change, one must imagine how things could be better. Staying stuck in the current moment, accepting the status quo as inevitable and permanent, is only a recipe for stagnation or the entrenchment of oppression. Imagining a better world may be no easy task, but it is one that we must undertake to begin to transform technologies.
A second framework that I frequently use in my work, one that is related to Davis’ call to not just imagine new worlds, but act collectively to create them, involves the concept of hope. Here, I do not refer to hope with a passive tone: hope that things change regardless of my participation. Rather, hope, in my view, is an active process. William James, a famous American pragmatist philosopher, described hope as a prerequisite to action, arguing that in order to take any action, we must first have some semblance of belief that we can succeed. By tying hope to action, our goal of making change thus requires us to believe that we can make change. Consequently, in the absence of hope, we are guaranteeing that nothing will change, never taking action.
Imagination and hope are two practices that we must take with us through the entire journey of learning how to make change. As we transition from the “problem” to the “practice,” we are compelled to be bold, creative, visionary. Especially given the cultural default many of us are saddled with, the idea that things are far out of our control, too big to tame, and that participation is a waste of time and energy, we must push even further in the directions of imagination and hope to counteract our bias.
Sites of Change
Before diving into examples and frameworks of change, I think it is important to acknowledge the complexity of change, and how my discussion of it will be oriented. Change can happen many ways. It can be unpredictable, nonlinear, complex. I do not wish to argue that any form of making change is inherently better than others, but I do want to convey that we all need to be critical of our intentions to make change versus our outcomes.
We should all strive to try to make meaningful interventions, and not be satisfied with something that may be disguised as change-making, but is truly self-serving or shallow. But at the same time, change is so unpredictable and complex that we should foreground the idea of accepting an ecology of change-making strategies, as a system of diverse attempts will likely succeed where a monolithic approach may not. I am inspired by my training from the Ayni Institute on this topic, a movement building organization located in Boston who write, think, and teach about making change. Their concept of “movement ecology” argues that often where we fight with each other over whose method of change is more effective, we would be better off by cooperating and acknowledging that employing many methods is a healthy way to organize ourselves.
When we think about change in the world of data science, or surveillance systems, we can identify at least a few sites where making change can happen. Engineers, workers, or academics can intervene at the level of the technical systems themselves: making algorithms more just, unbiased, transparent. Workers and activists can also intervene at the level of the institutions and organizations who employ data systems: organizing to stop unethical projects, guiding organizations in liberatory directions. Many can push for legislation that would force organizations to act a certain way under the threat of legal punishment for violation of the law. We can resist, find alternatives, and work together to make change at the level of the individual and interpersonal. And also, we can all work at the level of the society at large, working to undo systemic racism, sexism, technodeterminism, or any organized ideology that inevitably propels unjust technologies into existence.
Our discussions of enacting an ethic towards changing unjust data and surveillance systems will be organized around these sites. Each site of change needs the others, and viewing the entire picture as an ecology is a brilliant way to visualize the project we will undertake. We are intervening into a system, one which is interconnected and resilient because of its interconnections. If we imagine trying to radically change the dynamics of a forest just by cutting down a few trees, we quickly understand our folly. But if we imagine altering conditions of soil, sunlight, rain, selectively introducing new competitive species, removing others, we can see that there are many ways to affect a system and turn it to a new equilibrium. This is the mindset I want us to keep present as we progress.
Data Science for Collective Liberation
The first two chapters of Data Feminism that we read begin us on our journey of imagining, hoping, and strategizing. The first, concerned primarily with describing power and its impact on data technologies, introduces us to key concepts like the matrix of domination, and pushes us to ask key questions to figure out the current state of data science: Who is doing it? Whose goals are prioritized? Who benefits from it?
These questions can be fairly easily answered. For the most part, white men are doing data science. The goals of corporations, governments, and those with dominating social identities are paramount. And most benefits fall to those who already benefit from the system, those who enjoy privilege because of their social position. To conceptualize how power operates in society, D’Ignazio and Klein refer to Patricia Hill Collins’ matrix of domination, which theorizes domination operating in four domains: structural (law, policies, institutions), disciplinary (administration and systems that maintain oppression), hegemonic (culture, media, ideas), and interpersonal (our daily interactions).
Making change thus involves intervening in each of these four domains to ultimately dismantle technologies that aid or augment oppression and domination. Similar to the ideas in movement ecology, the matrix of domination hints at different ways of changing the status quo of data science and surveillance technologies. Laws and policies can be changed in the structural domain. Disciplinary systems can be dismantled or changed so that they are not able to use or benefit from these systems. Moreover, technologies that aid in disciplining a population can be changed. Education, alternative media, or advocacy can counter problematic ideas in the hegemonic domain. And we can each make interventions in the interpersonal domain by countering technological oppression of ourselves and each other.
But this is a very zoomed out view, and we should begin by understanding specific ways that technologies or data science work could contribute to challenging power. For one, not working on systems that further oppression in these domains could be a start, but we will discuss that in more depth later on. There are ways that data science is being used to subvert oppression, a sort of fighting fire with fire.
D’Ignazio and Klein helpfully illustrate several examples of data science projects that are challenging power and structures of domination. They point to Joy Buolamwini’s work detailing the skin tone and gender presentation biases of facial recognition technology. They uplift María Salguero’s project logging femicides in Mexico. They highlight the birth process community-building app Irth, ProPublica’s use of statistical analysis to critique the COMPAS criminal recidivism algorithm, the Our Data Bodies project, and the Detroit Geographical Expedition and Institute teaching their local community data science skills. These are all examples of data science projects that are empowering minoritized people, and working towards justice, equity, and collective liberation.
Data science has the capacity to aid in projects that are upending dominating power. Collecting information, analyzing it, making sense of it inside of a context, are all powerful steps that can lead toward liberation by aiding in processes of change. Many organizations need to collect and analyze data in order to make their cases to other powerful bodies that can enact change (which is, admittedly, often a problematic burden of proof). It may not be a perfectly satisfying use of data science, since it is playing catch-up with already existing oppressive systems, but it is a step in the right direction.
There is more to come as we continue to discuss change at the site of the technology itself next week, and begin to think about individual and collective resistance. As we keep this material in mind, we can exercise our imaginative muscles with some reflection:
Reflect: When you think about possible worlds that could be more just and free than ours, what types of technological systems do you imagine? Can you reimagine a system that we discussed in class, but rather than working to oppress, work to liberate or empower people? Give a specific example.
For next week
Readings and reflections
The reflection above will suffice for attempting to synthesize some of the material that we worked with for the week.
For next week, breaking the typical pattern of announcing readings in advance, the materials are changing, so I cannot tell you what they are! We will be reading and learning about theories of change, but I will publish those materials next week in the newsletter, rather than this week in advance.
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