Introduction
The gender data gap is not just a statistical error, but the systemic exclusion of women from the foundations of modern rationality. A world designed based on male data treats men as the universal norm and women as a "special case." This article exposes how this asymmetry affects medicine, the economy, and technology. You will learn why data neutrality is an illusion and how the lack of a female perspective leads to costly, and sometimes fatal, mistakes. This is a call to build systems that see the full picture of the human experience.
The data gap: a foundation of systemic exclusion
From a systemic perspective, the data gap is a power imbalance where women's health and labor become "cheap adjustment variables."
The default male model distorts medical diagnoses
In medicine, the male body is considered neutral anatomy. This results in misdiagnosis: for example, heart attacks in women manifest as nausea or shortness of breath rather than chest pain. Consequently, female patients face a higher risk of death.
AI algorithms replicate healthcare biases
By learning from historical, male-centric data, artificial intelligence reproduces discrimination. Language models are more likely to downplay women's descriptions of illness, deeming them less serious than those of men.
Biologizing differences masks social barriers
There is a risk that excessive biologization will obscure the social sources of suffering—such as flawed work organization or lack of care—reducing them solely to hormonal issues requiring pharmacology.
Care work: the invisible pillar of the economy beyond GDP
Unpaid domestic work generates value reaching half of GDP, yet it remains outside official accounts. The state treats it as a cost-free resource, allowing for the quiet transfer of budget cut costs onto the shoulders of women.
Taxes and pensions: mechanisms of financial degradation
Tax systems (e.g., joint filing) and pension schemes (participation thresholds) structurally discriminate against women, ignoring their specific professional activity and caregiving breaks.
The financial sector monetizes the lack of data on women
Credit algorithms, based on historical data, offer women worse terms, even though as investors they often demonstrate greater stability and reliability than men.
Urban mobility: trip chaining vs. the commuter model
Gender mobility patterns differ fundamentally: men more often move linearly (work-home), while women create trip chains (school-shopping-care).
Linear transport paralyzes women's daily lives
Designing cities for the commuter model makes infrastructure hostile to women. The example of Karlskoga showed that prioritizing snow removal on sidewalks instead of highways reduces social costs and the number of accidents.
Global North vs. South: geographies of data gaps
In Scandinavia, care is institutionalized; in the US, it is privatized; and in Arab countries, it is equated with a cultural role. Each of these models masks or reveals the data gap differently.
The myth of neutrality: gender-blind data is data about men
The logical contradiction lies in the fact that one cannot simultaneously believe in data neutrality and policy justice while witnessing real discrimination. Gender-blind data in practice always favors men.
Big Data: from visibility to the digital surveillance of women
More data brings risk: in authoritarian regimes, reproductive health information can become a tool of oppression and bodily control.
Summary
Women's representation determines the reliability of statistics
The presence of women in decision-making bodies is an epistemological prerequisite. Only lived experience allows for the detection of signals that the system has ignored until now.
A data constitution: the foundation of a pluralistic system
We advocate for the introduction of a mandatory sex-disaggregated data collection as a firm market standard. Data must become a tool for emancipation, protected by democratic institutions.
In the era of algorithms, will we manage to create fair systems, or will we entrench historical inequalities, locking women in a digital cage of bias? Will we learn to measure the value of the invisible before the data gap becomes an insurmountable abyss? Perhaps it is within these seemingly objective numbers that the greatest illusion of our time is hidden?
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