The Data We Trust Is Erasing Us—Here’s Why

February marks National Girls & Women in Sports Day, a time to celebrate progress in gender equity while recognizing the work still left to do. It felt like the perfect time to kick off my capstone blog series—a project that dives into some of the most eye-opening topics I explored during my gender advocacy fellowship.

So, I want to revisit a topic I touched on in my last post—one that stood out to me from the very beginning and ultimately became a core part of the feminist-focused curriculum I designed. I was introduced to a concept that completely reshaped how I think about information: data feminism. At first, it felt abstract—just another academic term floating in the realm of theory. But the more I explored, the more I realized it’s something we interact with every day.

Data feminism challenges the assumption that data is neutral. In reality, the numbers we rely on to make decisions—about policies, resources, and representation—are shaped by the people who collect them, the systems they operate within, and the biases they bring. One idea that particularly struck me was the “Library of Missing Datasets”—a powerful visualization of the information that simply doesn’t exist. There’s shockingly little data on issues affecting marginalized communities, such as maternal mortality rates among Black women. When data is missing, so are the voices of entire groups.

This opened my eyes to a deeper truth: addressing inequality isn’t just about having more data; it’s about ensuring the right data is collected in the first place. Who gets counted? Who gets left out? And how can we use data to drive real change instead of reinforcing the status quo?

With that introduction, I want to share some key aspects of my curriculum—real-world examples that illustrate how this concept plays out in action.

Note: This was a topic I had previously written about and researched extensively, but it wasn’t until my fellowship that I fully grasped how embedded this issue is in research itself. The way studies are conducted, the choices researchers make in interpreting data, and how they generalize their results all play a role in shaping medical outcomes.

Note: I’ve debated the complexities of facial recognition in the context of fairness and efficiency, and it’s clear this isn’t a simple issue. As our reliance on these technologies grows, we must ask: How can we ensure they’re accurate and inclusive for all? It’s about challenging the biases baked into the systems, not just tweaking algorithms.

Note: I hadn’t heard of counterdata before, but María Salguero’s work with ‘Mapa de Femicidios’ showed me how grassroots data collection can uncover hidden truths. It made me think about how much progress depends on filling gaps left by official data.

As Catherine D’Ignazio and Lauren Klein write in Data Feminism, “When data teams are primarily composed of people from dominant groups, those perspectives come to exert outsized influence on the decisions being made—to the exclusion of other identities and perspectives. This is not usually intentional; it comes from the ignorance of being on top.” This quote captures the core idea I want to leave you with: the power structures behind data are often invisible but deeply influential. These biases shape not only the technology we use but also the policies that emerge from data-driven decision-making. Looking at data through a feminist lens forces us to ask: who gets to define the narrative, and whose experiences are erased?

Transparency is critical, yet much of the data world remains proprietary and shielded from scrutiny. To challenge this, we need more education, advocacy, and a commitment to questioning the systems we rely on. By embedding these principles into data practices, we can push for more accurate, inclusive, and accountable systems.