Mimi Ọnụọha is an Igbo-American artist and writer whose work is about what happens when the world is made to fit the form of data. Her interdisciplinary practices uses print, code, installation, and video to call attention to the ways in which disenfranchised communities experience different relationships to systems that are digital, cultural, historical, and ecological.

Could you tell me a little bit about your backstory? How did you find your way into this work at the intersection of data, art, and research?

I went to university in 2007 during that era in which a lot of things were being defined when it came to social media and connectivity online. I did research focused on these newer social media platforms and the infrastructure that made them possible. But when I was about to start my last year, I realized “I’ve been thinking and talking and doing all this independent work about these technologies but I don’t know how to actually create these systems, and I wonder if there’s something I’m missing by not really understanding that language.” I was like, I gotta learn how to code. That’s it. But I thought, “Oh, it’s too late,” because that’s what you think when you’re 21. The only program that I found that you can come here not knowing anything and we will teach you how to code was this program at NYU called ITP. So I applied, got into that program, and went there directly after college, and it happened to be in an art school. It happened to be part of Tisch.

While I was there I was trying to learn more about the decisions you make in the moment have all these ramifications for users down the line, or just anyone down the line, and doing that within an art environment. Art and research for me just ended up being very tied together. So while I’m exploring and trying to make sense of this, I’m also creating these artworks.

I did this project the year after I graduated. I was getting catcalled a lot in the summer and wanted to do something where I had more agency. So I would give anybody who catcalled me a piece of paper that had a phone number on it. They thought it was mine, but really it was set up to a service where they would get these strings of messages that I had programmed. That gave me this sense of being able to leverage the disconnection of technology and still feel very intimately tied to these people. At the end of that project, I realized I had this dataset of my catcallers. And they’d sort of opted into it, but without really knowing the terms upon which they had done that. And then I was like, “Oh, okay, this is interesting! All of these things can align.” I was doing this as a kind of art project. But actually comes from other research I’ve been doing. That project was so foundational for me in terms of getting me into thinking a lot more about data and data collection and the relationships inherent within. And so that is how I’m here, 10 years later.

That catcalling project actually really got me thinking about what it means to practice subversive data science. I really like to think about things in terms of this collective liberation idea, that all of our struggles are intimately connected. So, what role do you see data science playing in our collective liberation, if any?

I’m of the opinion that all technology, not just that we recognize as part of the tech sector, is always created in service to something. And if you’re not clear about what aims the technology is going to be created in service to, then it will just default to supporting the dominant model of power that exists at the time, or the means to which you can attain power. So when it comes to data science, as a set of tools, I think absolutely it can be used. If it’s created under terms of liberation, where you’re really thinking, “Alright, well, this is why we’re using this,” then I think it can actually help get you there. At the same time – and of course this is the nuanced part of it – there’s a way in which data science sees the world that is necessarily reductive. But that can be useful sometimes. The problem is when that’s overextended and applied everywhere, as opposed to the places in which it is very useful.

So much of your work interrogates notions of messiness, absence, and that which cannot be captured in data. But as you already alluded to, the data science community really emphasizes this need for simplicity that can sometimes be overly reductive. So to you, what’s the importance of adding nuance and complexity back into our thinking and our models?

I’m forgetting the saying…every model, none of them are true, but some of them are useful? I think that’s very true. It can be useful, particularly over the aggregate, to have some way of making sense of things and see patterns and trends. It’s just that you still have to deal with that question of what’s missing or what’s on the edges. And I think that is the point at which, for me, I just love focusing on those places, the edges, absence. I think that for any system, looking at what’s absent, or what seems to be absent, really reveals what is prioritized. But it’s not always like, oh, whatever is missing isn’t important. It isn’t necessarily that way; something that’s missing can be extremely important. It can be that somebody wanted it to not be there. Looking at that absence helps make sense of the wider system and anything situated within.

I’d really love to dig in a little bit more to your project In Absentia which focuses on the work of W.E.B. Du Bois. You said in a recent interview, regarding the drawings you created for the exhibition, “They’re communicating something about data, but they’re really about the conditions of a world where you need these sorts of infographics more than they are about the data itself.” I was just hoping you could expand upon that a little bit more. As someone who’s done some government work, where you’re constantly having to prove the importance of certain data points, I am fascinated by this idea of interrogating the conditions of a world where you are constantly asked to prove injustice with data.

In Absentia is an exhibition, but it’s also the title of six prints that I made that are in conversation with Du Bois’s work. And in mine, I just reject even the idea that I should be visualizing something that is tied to improving the condition. And instead I ask, well, why are we making these? What does it matter? What does this language afford? Why is there strength in it? I was really fascinated with this idea Du Bois is coming to terms with who is this proof for? And what do I do if the thing getting in the way of people’s understanding is not the data at all but the world that they live in and white supremacy and what they can allow themselves to see without taking that much away? So that project was one of those ones that was really captivating to me. And I was trying to really work out what it was by making more. And in the end, I think it is my own fascination with the moment when you have to use data to try to talk about the condition of some people because you know that the way that they’re being treated on its own doesn’t count; you have to now tap into some kind of power language. Data operates in the same way that legal language operates as a power language.

Let’s talk a bit about this “data collection as relationship” idea. You often have this group that wants to collect something, and the group that makes up the collected. I think recently there’s been some movement towards trying to dismantle that distinction between researchers and subjects. Do you have any thoughts on how that process is going or where you would hope it’s going next?

We’re in relationships with lots of things and people and places and systems, but you can’t see it very easily. From one person’s point of view, many things just seem like products. So it’s hard to see that actually there is something on the other end and that you are now in this relationship with that thing. The most interesting and useful thing for me to do early in my work was to just make clear that relationship. And the reason I thought that was so important was that having that framework of data collection as a relationship allows you to understand tensions that could arise.

For instance, thinking about the census. Collecting data for the census is incredibly important for so many different groups. At the same time, when you think about this “data collection as relationship” framework then you start asking, Who’s represented and which groups? Who has a say over the collection? What’s the history of that relationship? Then you can understand why some people have this reticence towards the census. That makes sense, historically. I think that when you can get a situation where the people collecting are the same as the people who are within the dataset, it honestly changes everything. All of a sudden the stakes are shared. You see so many fewer issues when those two groups are the same. But honestly, most of the time, it’s not that way.

The “data as relationship” model helps you understand why there might be trepidation, but it doesn’t help you get over it. And it doesn’t help you fix it, which is actually the more important thing. So I think that for me, this is part of why I started to wonder:How do we move forward? A lot of my early work was more about “let’s make sense of this” and then realizing the limits of that. That isn’t any kind of theory of change right here. That is just about understanding.

So what have you been exploring recently? And where are you kind of headed next?

Oh, I don’t know if I’m as interested in providing evidence of something that is already here. I don’t know if I’m interested in proving a thing that already exists. I think I’d rather go to the spaces with folks who already understand that and see what interesting things we can do and see what it looks like to create this kind of multiplicity of approaches. What it looks like to undo these hierarchies and try to explore the things that have been suppressed. I think that thinking about technology moving forward requires a deep attention to the past and especially those contexts, cultures, histories that were suppressed. But it also requires knowing that we are not in that time and this is a different moment. We can’t undo the internet – we live with it, so we need to take our histories and bring them into this moment. That means we have to have a different lexicon and a different way of responding and articulating what we want to see. And so I think all of the new work I’m doing really is all about that. It’s like, what does it look like to articulate that? What can we pick up from the past? What do we need to know? What gets in the way of us doing that? Who is the “we,” who is the “us” in any situation? What are those preconditions that are laying the foundation for what we want to see? What isn’t? How do we hold all of that? And that’s what all of the new work is about, I think [laughs].

You just literally gave me chills! I feel very excited for that and just excited to explore your work more and see how it develops. One last question: What kinds of folks do you hope to inspire in future generations?

I don’t know who I’m interested in inspiring. But I love expanding the idea of what is possible and what is important and what we can do. And, if anyone in the future is like, “Great, that was helpful to me and now I can run this way with my collective of people here,” or, “Now I can take this and do something else,” that’s perfect.

This interview has been shorted and edited for clarity and readability.

In Natural: Or Where Are We Allowed To Be, a Black woman makes her way through a data center that carries her own information. Surrounded by towers of server cabinets, she searches for answers to the question of what her relationship to the place should be. Inspired by British-Caribbean photographer Ingrid Pollard’s “Pastoral Interlude” prints that feature Black Brits asserting their right to be in the English countryside, in Natural: Or Where Are We Allowed To Be, Ọnụọha questions ownership over server rooms and data at large. She writes, “In data sets we appear as the perfect subjects: silent, eternally wronged, frozen in a frame of injustice without the messiness of a face/accent/hint of refusal….When structural workings of racism meet the distancing power of quantification, both combine to freeze us in place.” This work was created with the support of Pavel Ezrohi and Tinuade Oyelowo.