Why Might Others
Experience This Collection Differently?

Perhaps some people have never been injured in a car accident, so they don't question why crash test dummies are defaulted to "average men"; Perhaps they have never been misdiagnosed by doctors, so they can't see why medical research ignores women's physical differences; They may find urban transportation "convenient" because this space is tailored to their bodies and behavioral habits.

Many people are used to thinking that "data will not lie" and "science is neutral." But in fact, data is not a simple "discovery" of reality but a social choice - it determines who is recorded, who is excluded, and whose experience is considered "worth keeping."

As Danah Boyd and Kate Crawford (2012) point out, 'data are not generic. They are artificially created, selective records of “things worth recordin”g." This means archives are not naturally occurring but are human constructs in which bias is systematically embedded. The collection, classification, and cleansing of data are not neutral technical processes but social behaviours that are deeply influenced by power structures.

Groups that are considered “irrelevant”—such” as women, marginalised people, and low-income earners - are often omitted from data and are collectively ignored in policymaking, technology development, and the allocation of social resources. This ‘absence’ is often overlooked in policymaking, technology development, and social resource allocation. This “absence” is not an individual failure but a systemic consequence.

Therefore, we should not only pay attention to what the statistics ‘say’ but also consciously question, "Who develops the logic of statistics? Who are these data for?" Feminist approaches to data remind us that doing research is not just about generating graphs and models but also about interrogating the logic behind them - who has the power to define the structure of the data? Whose voice is structurally excluded?

Those who do not have to think about diversity are those who do not have to think about being included (Ahmed,2012).So this is not just a historical issue—it is a deeply contemporary one. It affects not only women, but all marginalised, misrepresented, and structurally excluded individuals. If they are missing from the archives of the past, they risk exclusion from the systems of the future—algorithms, policies, urban infrastructure, healthcare.

The absence of data means an absence in reality, and historical injustices are being deposited in new ways in our technological and social structures.To rewrite the record is not just to remember history—it is to design a more just future.