Beyond the binary checkbox in gender justice: How binary data silences trans* and non-binary experiences

Introduction: Gender Justice Isn't Justice for All

Gender justice is often talked about in neat binaries: male vs. female, men vs. women, patriarchy vs. feminism. But what happens when the data, systems, and policies built on those binaries ignore people who exist outside of them? What happens when trans*, genderqueer, and non-binarypeople are rendered invisible by the very institutions meant to fight for gender equality?

In this essay, I argue that gender justice that relies on binary data is not justice at all. It’s a system of partial recognition and selective liberation. When policies, research, and activism fail to account for the full diversity of gender experiences, they reproduce the very inequities they claim to dismantle. It’s time to confront the gaps and to ask ourselves hard questions about what we mean when we talk about fairness.

A people made invisible: Who gets counted?

Let’s start with the numbers. In the U.S. alone, the Williams Institute reported that 1.2 million adults identify as non-binary. The Trevor Project found that over one in four LGBTQIA+ youth say the same. In countries with emerging data, like Australia or parts of Europe, figures vary, but one truth is consistent: a significant number of people don’t identify as either male or female.

Yet most surveys, censuses, and assessments either offer two gender options or ignore gender identity altogether. That means decisions around healthcare, education, employment, and justice are being made without knowing how they affect millions of people.

This is not a data issue. It’s a political choice to not look. And when we don’t count people, we tell them they don’t count.

 
The binary bias: Why data still gets gender wrong

Traditional data collection relies on what’s convenient, familiar, and statistically comfortable. Most institutions ask one question: "What is your gender?" with two checkboxes: male or female. Some progressive ones who have upgraded their HR policies sometimes offer the "diverse" option, which is too vague and remains open to interpretation. This erases lives that don’t fit and feeds into the illusion that they’re rare exceptions.

Progressive institutions now increasingly use the two-step model: ask for sex assigned at birth, then ask for current gender identity. This simple shift, endorsed by major health and research bodies like the U.S. National Academies, yields more accurate, inclusive data.

Yet global adoption remains inconsistent. India has made strides, including a third gender category in official documents. A high court in Brazil has recently allowed people to identify as gender neutral on official documents. In contrast, other countries criminalise gender variance, consequently weaponising data collection to surveil rather than support. It’s a reminder that inclusion isn’t just about options, it’s about trust, power, and safety.

The cost of erasure: Policy gaps and real-world harm

When trans*, genderqueer, and non-binary people are invisible in data, they're invisible in policy. Consider health: trans* people are less likely to receive appropriate reproductive care, often forced to choose between being misgendered or denied access. Non-binary folks on the other hand face similar hurdles, especially in mental health systems not trained to support their realities.

Or look at employment. Without disaggregated data, we can’t quantify workplace discrimination, wage gaps, or hiring biases faced by gender-diverse people. Anti-discrimination laws in many places still don’t explicitly protect non-binary identities, partly because the data isn’t there to make the case.

This isn't just a matter of fairness. It's a matter of survival.

 
Intersectionality matters: Layers of oppression, gaps in understanding

The harm is amplified when gender diversity intersects with race, class, disability, or migration. A Black non-binary person seeking asylum faces barriers that are wholly different from a white (white passing too) cis woman in the same system. But binary-focused data tools often flatten these differences, missing the nuanced realities that would make interventions effective.

Without inclusive, intersectional data, policies become blunt instruments. They might serve some women, but leave trans*, enby, and intersex people out in the cold.

 
Beyond inclusion:

Counting is not enough. Ethical inclusion means:

Community involvement: trans*, intersex, and enby people must help design the questions.
Privacy and protection: particularly in countries where disclosure could mean violence.
Legal recognition: without identity documents that reflect who you are, access to rights is always conditional.

This is where data becomes radical: not just descriptive, but transformative. Participatory, inclusive research practices challenge power, redistribute voice, and build systems from the margins in.

 
Final reflections: What do we really want from gender justice?

If gender justice is truly about fairness, then we must ask: fairness for whom? Are we content with metrics that improve life for cis women while leaving trans* and enby folks behind? Can we claim victory in gender equality if our tools ignore millions of people?

Justice must be collective, not conditional. It must be messy, uncomfortable, and fully human. That means abandoning the fantasy of neat categories and embracing the full spectrum of gender. It means recognising that the binary isn’t broken: it was never built to hold us all.

Remember, data is not neutral. It reflects who we care about. It shapes who gets help and who gets left out. If we want real gender justice, we need real data justice: inclusive, ethical, community-informed systems that count everyone.

Because when we fail to count people, we fail to protect them. And no justice worth its name should leave anyone behind.