In the US academy, the fields of computational cognitive science and neuroscience are saturated with funding from the Department of Defense. Why do US scholars in these fields—who study the brain, the mind, and sometimes prospects for their interactions with computers, and who are otherwise outside of the US military-industrial complex—choose to accept research funding from the primary military supervisory branch of the federal government? To approach this question, one could consider the ways military and history are entwined with K–12 education and the textbooks that inform it: no one is about to defend the notion of public school education as anti-imperialist or critical of militarism. One could also consider the ways that STEM (science, technology, engineering, and mathematics) education within universities tends to consider conceptions of the world abstractly, such that the interaction between STEM and systems of racialism, imperialism, and militarism are nowhere to be found. Perhaps researchers are not particularly aware of the ways military artifacts and outcomes make their way into domestic policing apparatus that are then deployed for violence against and containment of racialized—especially Black and brown—and economically exploited communities. Thus, it is reasonable to assume that early career scholars aren’t always equipped with a nuanced understanding of how their work connects to racialized, imperial systems. Even when they are, one might infer, correctly, that they often act less from a concern with these complicities than from the standpoint of what is best for their career. However, if we zoom out and expand our view beyond individual educational and knowledge-transfer impacts, we might perceive the ways that both national funding priorities, and organizational policies and values at universities, can mediate the ways many researchers at universities choose to conduct their work. This last point is especially true at so-called R1 universities.
The designation R1 comes from the Carnegie Classification of Institutions of Higher Education, a framework of categorization that holds sway among academic circles. For those universities that award PhDs, the Carnegie Classification has three main categories: doctoral universities with “very high research activity” (R1), doctoral universities with “high research activity” (R2), and doctoral/professional universities (D/PU). For academic faculty who want to conduct a high volume of research, R1s tend to be the most coveted jobs, and new faculty who hope to have a stable job in a STEM department at an R1 (through the process of tenure, which makes it more difficult to be fired) need to secure external funding for their work. This isn’t just to do the work—though money certainly is needed to do that—but also because external funding is associated with a certain prestige. Therefore, organizations and communities of faculty will prioritize acquisition of external funding.
So, when a professor, heavily influenced by their department, universities, and all that they’ve learned before they began their career needs to go out and secure funding, where do they turn?
Unbalanced Funding
In 2021, the National Science Foundation (NSF, an independent US federal agency that funds and supports science and engineering) had a $6.4 billion budget. It would stand to reason that this agency would be a great place to turn when seeking science and engineering funding. In many ways it is, and researchers at universities from all over the US apply for funding from NSF each year. For example, I’ve been awarded over $1 million in funding from NSF for my own research, and the (R1) university at which I have my faculty job—Penn State, where I run the Human in Computing and Cognition (THiCC) Lab, researching intersections between Blackness and AI—was awarded about $75.5 million in funding in the 2023 fiscal year. However, when it comes to funding from the NSF, there are two major caveats for computational cognitive and neuroscience researchers: first, the NSF does not generally award any funding for research that could be considered “medical sciences,” which may include some research in areas such as brain–computer interfaces (the subject of the tech explainer by Andrea Stocco that appears in this issue of Logic(s) ) that would then need to seek funding from another agency. Second, NSF funding only accounts for a small amount of research and development (R&D) funding given out each year; in fact, the NSF budget was only 4 percent of the total US R&D budget for the 2021 fiscal year, meaning the majority of obligated funding derives from other federal agencies.
So, where does most of the R&D funding come from? The vast majority (77 percent in the 2021 fiscal year) comes from the Department of Health and Human Services (HHS, 38 percent in 2023) and the Department of Defense (DoD, 39 percent in the 2021 fiscal year). If we couple this with the idea that plenty of research in computational cognitive and neuroscience will not be “medical” (and thus may not fit into the goals of the National Institutes of Health [NIH], which is where about 94 percent of HHS funding goes), and the well-documented lower success rates and paylines for NIH-funded research, which has been especially true among Black scientists, we can see how receiving funding from the DoD would seem natural1 to a faculty member who lacks a critical lens regarding its implications for the uses of their research and the policies their successes implicitly support. When it comes to the latter, we must remember that research funding is always political, and that a successful research project may be used not only for direct military applications but also to justify to legislative bodies (such as the US Congress) the continued wide disparity in research-funding sources and applications.
Those faculty who choose to go the more difficult route and avoid 77 percent of the R&D funding sources in the US government (i.e., those from the DoD or NIH) have to seek funding from other federal sources, like the aforementioned NSF, or corporate contributors. Even private corporate funding may have problematic military connections or bring with it other issues, such as those discussed by Meredith Whittaker, president of Signal and cofounder of NYU’s AI Now Institute. This is not to say that this route is impossible, especially among the relatively well-resourced R1 universities, but merely that organizational and national processes and policy often work to redirect individuals toward funding from sources like the DoD.2 Indeed, adoption of a critical perspective on funding choices is possible—even for faculty previously connected to US military systems—but it is more labor intensive, entailing work that all too often lies outside of the research infrastructure universities provide.
A Change of Path Is Possible, but Potentially Difficult
Even some of the more critical voices in academia may have a past directly connected to military funding. After all, the promises of military service, such as through the Reserve Officers’ Training Corps (ROTC) or one of the numerous full-ride “scholarships for service” (which require future service as a civilian) may be seen as providing reasonable paths to improve one’s financial circumstance. Historian Paul Ortiz, for example, has also written on the ways his years in the US Army Special Forces impacted his own scholarly engagement with the Black radical tradition. Another example, perhaps more germane here, is my own path to becoming a researcher at the intersection of computational cognitive sciences, artificial intelligence, and engineering—a career trajectory which at multiple junctures drew on financial support tied to the defense industry.
I grew up in a working-class family, in an economically vulnerable single-parent household. Though I was too young to remember it, my parents both (briefly) served in the US Air Force. So, without giving much thought to alternatives, I made the seemingly obvious decision to accept a full-ride “scholarship for service” to pursue undergraduate studies at a public university, Penn State. (“Say word, I get my tuition covered and a stipend that gives me some extra money to help out the fam?!”)
Later, as an early career faculty, I engaged with some DoD-related funding without much critical thought to those processes that led me there, or to the nth-order effects of using those funds. My research during this time was focused on exploring the ways stress and lack of sleep affect the way people are able to learn, think, and behave—topics of interest to agencies within the DoD, given their importance to training and accomplishing tasks when folks are in stressful situations and under various sleep schedules. My engagement with those opportunities was certainly led by the cultural value in academia placed on achieving funding, and had I been at a university that was classified as R1 (I’ve only moved to an R1 within the last few years), there is a chance I would have engaged much more with DoD-related funding, without a critical intervention (whether through introspection or otherwise). Fortunately, I found other paths of scholarship and have been able to progress in my own career over the last few years with funding mainly from the NSF. Nonetheless, this explicit pivot in funding choices (and, indeed, a pivot in my own research toward studying the ways AI systems are built and exist within the context of anti-Black logics that exclude many people from being considered “human”) came with a risk that will remain in play: if the more difficult-to-access funding sources (such as the NSF) don’t work out, the impact I can make within a university research context will be, in many ways, reduced.
This is all to say that faculty certainly have the power and agency to make conscious decisions about what funding sources they choose; however, the nuance of what it means to be a faculty within the large, powerful organizations that are universities matters. For example, the values and processes with which those universities encourage faculty to engage help determine which paths they are most likely to take. Even for those faculty presented with seemingly ironclad funding paths, alternatives are available. It is up to faculty in the computational cognitive science and neuroscience fields to make more conscious decisions regarding the institutions with which they choose to engage, just as it is up to those faculty who have the power to make change at the organizational level to better encourage nonmilitary paths for research funding. If faculty researchers are to break the cycle of dependence on funding from the US military-industrial complex, change must take place at the level of individual choices, organizational structures, and policy alike.
1. Here, when I say “natural,” I mean in the sense of the ways a racialized capitalistic perspective permeates organizational values.
2. Note that this doesn’t necessarily include other potentially related, problematic sources of funding from US agencies like the Department of Homeland Security or federally funded research and development centers.