Sasha’s Project Journal (2/18/2026)

Our team carved out some time outside of class to sit down and actually talk through what we’re doing together. It sounds simple, but there’s something really valuable about getting everyone in the same room (or virtual room) before a project picks up steam, just to make sure we’re all working from the same map. The core idea behind our project is something I find genuinely fascinating: instead of treating AI hallucinations as bugs or glitches, we’re reframing them as data worth studying. Specifically, we’re looking at how large language models distort or outright erase the histories of marginalized communities, and what that tells us about the biases baked into these systems. It’s a shift in mindset from “the AI made a mistake” to “the AI revealed something.” That distinction feels important.

I am particularly excited to dive into the technical side of this by developing an interactive user interface to explore our data. Since the project will eventually be a public-facing web archive, I want the searchable database to feel intuitive while allowing users to filter “lies” by demographic and error type. Creating data visualizations that illustrate how AI hallucinations vary across different cultural contexts will be a challenge, but a necessary one to move from a “glitch” mindset to “critical data”.

Also, getting outside perspectives on the project this week helped surface some of its more subtle aspects. We’re essentially treating AI hallucinations as cultural artifacts, that opens up a lot of questions we’re still working through. When an AI invents a citation, is that meaningfully different from when it invents a historical figure wholesale? What about erasure by omission, when someone’s history simply doesn’t appear, or appears only in distorted fragments? Are these all versions of the same problem, or do they need to be categorized differently? I don’t think we have clean answers yet.

Because I’m the person most familiar with the project at this point, especially the technical side and the prior dataset work, there’s an added responsibility on me to keep the structure clear and the vision consistent. That doesn’t mean it’s “my” project, but it does mean I need to help translate ideas into concrete next steps and make sure everyone’s aligned. As the project manager, I want to be clear about timelines and decisions without being overbearing. I want to keep us moving while still making space for everyone’s ideas to shape the project. I can already see how striking that balance may be more challenging than it sounds, but it feels like an important skill to build.

Right now, the main focus is keeping our workflow steady as we get further into the project. The first few weeks of any collaboration usually run on excitement alone, but that energy fades. I want us to build habits that will still work when deadlines stack up or things get complicated. For us, that means regular check-ins, being honest about what’s working and what isn’t, and paying attention to how the workload is distributed so no one quietly takes on too much. We’re still early in the process, but I feel confident about the team we’re shaping and genuinely interested in where the research will take us.