By Saumya
What if every shoe in the world came in just one size? Not small, not large, just average. If your feet hurt, pinched, blistered or bled, the problem wouldn’t be the shoe. It would be you. You would be told to adjust, to get used to it or quietly accept the pain. Over time, you might even start believing that hurting is just part of walking.
This is the literal reality of how we have built the world of menstruation. We are living in a “one size fits all” era and as we slowly hand more of our healthcare decisions to apps and artificial intelligence, that average shoe is about to get even tighter.
Menstruation is a biological masterpiece of variability. If you sit with ten people who menstruate in a room, you will find ten different experiences. Some experience a light, breezy cycle that looks like just another day while others face a monthly storm so violent it stops their ability to work, sleep or go to school. Cycle length varies. Hormone levels don’t just rise and fall, they dance like crazy. Symptoms change month to month. Even bleeding differs on different days of the same cycle.
Menstruation is not only experienced by women. Trans men and some non-binary people menstruate too, often under very different social and physical conditions. Imagine bleeding every month in a world that insists only “women” bleed. Imagine needing pads but feeling uncomfortable entering a “women’s hygiene” aisle. Imagine the dysphoria of a body doing something society has rigidly gendered. Biology does not create a single menstrual experience, yet society keeps forcing it into one neat, pink box.
This flattening begins with a well-meaning but flawed reliance on the “average.” Science depends on averages because they are easy to measure and compare. But they can also mislead. If one person’s value is 2 and another’s is 1000, the average is 501, a number that represents neither. In medicine, designing for the average may be convenient, but it is rarely safe. Real health risks exist at the margins: heavy bleeding, irregular cycles, severe pain, limited access to sanitation, or bodies that don’t fit the idea of “normal.” When we design for the middle, we exclude those at the edges.
The problem is reinforced by how menstrual data is collected. Much of it relies on self-reported surveys that reduce complex bodily experiences to simple categories. Period-tracking apps, for instance, often ask users to classify their flow as “light, medium, or heavy.” This is like describing a thunderstorm as “slightly wet” or “moderately wet.” The body does not operate in drop-down menus. Mood is not just “happy, sad, or neutral.” Yet research often focuses on what is easiest to quantify: cycle length, frequency, regularity. These fit neatly into spreadsheets, but they fail to capture the full complexity of lived experience.
Where is the box for pain so sharp you can’t sit in class?
Where is the question about having privacy to change a pad?
Where is the space to mention gender dysphoria while bleeding?
What we don’t measure doesn’t disappear. It becomes invisible, and what science overlooks is often dismissed in real life. These assumptions don’t stay in journals. They shape how pain is labelled, how policies are written, and even where vending machines are placed, often only in women’s washrooms.
For a non-binary student or a trans boy who menstruates, that is not a small detail. It is a monthly reminder that the system was not built for them. If you don’t fit the “average,” the system turns it into your inconvenience, something you are expected to manage quietly.
Now, enter artificial intelligence. You don’t need to understand coding to grasp this. AI learns patterns from past data to predict what comes next.
Think of teaching a child what a bird is by only showing sparrows. The first time she sees a peacock, it doesn’t fit the pattern. AI works the same way. What appears frequently becomes “normal,” while rare experiences, like a trans man’s cycle or endometriosis, become statistical outliers. And outliers are often treated as noise.
This isn’t malice. It is mathematics. When a health app claims “95% accuracy,” it sounds reassuring. But 5% of millions is still millions of real people whose bodies don’t fit the model. To maintain accuracy, systems prioritise the majority, quietly sidelining those who don’t conform.
This is where it becomes dangerous for common people. If your period app tells you your symptoms are normal, you might doubt your own pain. If it predicts your cycle incorrectly every month, you might blame your body. If it never accounts for the fact that you are a trans man using testosterone, it may treat your experience as an error instead of a variation. Technology feels objective. But it reflects the data we feed it. If that data was narrow or incomplete, the technology will carry those same blind spots forward. And this is not just about apps. It’s about everyday life.
It’s about the student who misses class every month because their cramps feel unbearable, yet everyone says “it’s normal.”
It’s about the trans-boy trying to make products designed for someone else’s body work for him.
It’s about the non-binary person who avoids public washrooms because they do not feel safe in gendered spaces.
When systems are built for the “average woman,” they erase diversity within womanhood and erase everyone outside of it. When someone is repeatedly told their pain is normal or their experience is rare, they slowly begin to believe that they are the error.
So what do we do?
We need a science that values thoughtful data, not just more data. Instead of rigid checkboxes, we allow descriptive experiences. Instead of ignoring the 5%, we design for them too. Inclusivity is not decorative. It is a safety feature. And this responsibility is not limited to researchers or tech companies. It includes all of us.
It shows in how we speak. When we say “only women get periods,” we erase people. A shift to “people who menstruate” may feel unfamiliar, but for someone listening, it can feel like recognition. It shows in how we respond to pain. Instead of “it’s normal,” we can say, “That sounds hard.” Validation matters.
It also means noticing who is missing. If products or policies assume one kind of body, question it. These are not attacks, but expansions. And when using health apps, remember they are guides, not authorities. If something feels off, trust your body. Data can inform, but it cannot replace lived experience. Ignoring an “outlier” to keep the math clean always means leaving someone behind.
If we keep building for the average, the future will look efficient and polished but unequal. But if we design for the messy, beautiful edges, science becomes humane. It’s time to stop shrinking bodies to fit models and start building systems big enough to hold all of us: women, trans-men, non-binary people, the heavy bleeders, and the irregular cyclers.
Because hurting should not be part of walking. And invisibility should not be the price for being different.
(The writer is Third Year BTech Student, Plaksha University)
