Why Our Digital Tools Are Never Neutral
DH 500 - Class 9 - 2025-11-04
It’s Week 9 of DH 500, and my brain is still processing the heavy theoretical lift we did this session. We tackled the essential, uncomfortable question of Digital Humanities: Are our tools—the very infrastructure we rely on—fundamentally biased?
The Great DH Debate: Criticism vs. Code
We kicked off by revisiting a crucial point from last week’s reading: the assertion that displacement (moving texts from analog to digital) is neutral.
This stems from an old debate in DH: the “Program or Perish” argument. On one side, some assert that if you can’t program, you can’t be a digital humanist. On the other side, scholars argue for the priority of criticism—that digital tools must always serve the purpose of critique (in the Marxist sense): examining history, culture, and literature to understand race, gender, and embedded politics.
The conclusion we reached about displacement is nuanced: The movement itself may be neutral, but the manner in which it’s done will not be. We have to be vigilant that DH doesn’t simply rewrite cultural heritage to fit our present-day categories instead of those of the past.
This is why, as a field, we must embrace digital tools for their access and openness, but always approach them with suspicion and wariness about who’s profiting and who’s being erased.
When Technology Mirrors Society: The MacPherson Analogy
The MacPherson reading drew a powerful, non-causal parallel: the development of technological structures and social structures co-occur. MacPherson suggests that the development of modularity in technology, exemplified by the operating system UNIX, co-developed alongside the societal idea of race-blindness/colorblindness.
Since the people building UNIX likely weren’t inclusive (few people of color or women in the room), the system they created subconsciously replicated that exclusive environment. This is a chilling thought, and we extended this analysis to our current obsession: AI.
AI as Neoliberalism’s Capstone
The current, massive $3 trillion investment bubble in AI feels reminiscent of historical bubbles (printing press, railroads). We discussed the view that AI is an extension of neoliberalism—an ideology prioritizing capital, free markets, and hyper-individualism.
If AGI (Artificial General Intelligence) achieves the goal of outperforming humans in economically valuable tasks, capital can simply replace labor with robots. Consumer-facing AI, meanwhile, perfectly aligns with the age of instant gratification and plunging attention spans, acting as a kind of “capstone” to this system. It forces us to ask: What societal biases are being replicated and solidified by these new, powerful structures?
Infrastructure: The Support System We Can’t Lose
We ended the day with a critical lecture on infrastructure. Unlike a project (which has a beginning and end), infrastructure is a continuous support system that requires maintenance and on which other services run (think roads, libraries, or fiber internet).
The humanities are currently undergoing a generational transformation of our knowledge infrastructure, moving from an analog set (physical archives, libraries) to a digital stack.
The danger is real: Historically, in the rush to digitize, some libraries destroyed physical collections (like microfiched newspapers) because the digital copy was deemed sufficient. This is a huge mistake. The digital version is not always the same as the physical original. We need to ensure we don’t lose valuable elements in this transformation.
This includes new challenges, like preserving video games, which means libraries must now maintain the hardware platform (the consoles) as well as the digital files—a constant, fragile undertaking.
Final Thoughts & Avoiding AI in Critique
The session was a solid reminder that while we must learn to code and build, the critical perspective is our main contribution as humanists.
In fact, the instructor explicitly told us to avoid using AI to write our weekly reading responses. The entire purpose of that assignment is to develop our own voice and share our unique thinking, something a large language model can never truly replace.
Now, if you’ll excuse me, I need to go practice my presentation skills and remember to treat my knowledge as a “gift” to the audience!