This article is written by Silvana Fumega, research and policy director, ILDA

Often when we speak of data, we refer to how algorithms use it, and the standards and legal frameworks that shape its production. But data is also the basis for designing policies and tools that solve public problems.

In this sense, if we don’t use the right categories to understand why we’re collecting data, we won’t collect the right data. Without it, designing policies that offer solutions to diverse groups of people and, even more broadly, that promote social change, is incredibly difficult.

This gives us a clear idea of ​​what happens when we discount certain people or groups in our data choices: it also sharpens our focus on who we are leaving out of our account. It shows why we need accountability and to think about inclusion when it comes to data production.

What does inclusion in data production mean?

First of all, when talking about gender data, it is important to notice people often use binary categories to describe sex instead of using gender categories. This means that sex-disaggregated data is collected and presented in reference to the biological differences between males and females. Having this type of disaggregated data is important, but there is much more to add. For example, if a person’s genetically-assigned sex does not line up with their gender identity, you might end up leaving them out of the data completely, or using categories that do not reflect their identity. Neither is a good idea.

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I work at ILDA, an organisation that seeks to understand and promote data policies in Latin America, and to use data to further the inclusive development of the region. In one of our projects — regional standardisation of data on femicide — we found that if we restrict the data to a binary sex category we are making many populations invisible. If a transgender woman is unable to change their gender marker on their official identity documents, then, if they are victims of a homicide, it might be difficult to identify them in official statistics on femicide. This of course depends on the cultural context of the country, and some might register such deaths as transphobic femicides. But we see that when we don’t take a feminist approach to data production, and the use of data for policy purposes, we risk making certain populations invisible.

We, as a society, must be aware of the implications of the data we produce and consume.

Binary sex categories have other consequences. As academics Catherine D'Ignazio and Lauren F. Klein discuss in Data Feminism, when it comes to creating an online user account, male and female are often the only available options. Forcing people to make this choice has consequences. For the estimated nine to 12 million non-binary people in the world, the seemingly simple request to “select gender” can be difficult to answer. Quoting Maria Munir, the renowned British activist, D’Ignazio and Klein write: “If you refuse to register non-binary people like me with birth certificates, and exclude us in everything from creating bank accounts to signing up for mailing lists, you do not have the right to turn around and say that there are not enough of us to warrant change”.

In official statistics, the data collected and disseminated still refers, in most cases, to binary sex categories. This leaves many populations invisible and underrepresented. The cost of binarised statistics is ignoring the experiences of many individuals who don’t conform to those two categories. If we don’t broaden our approach, we will keep looking at the world in a binary way, never capturing the fact that people and their realities are much more complex.

Data should count all of us

The issues we have discussed so far all cohere around the idea of ‘data for inclusion’. The concept behind the work being done to improve data production and publication is that the data should count all of us, not just a few.

Beyond gender data, we might encounter other marginalised groups which have been made invisible in official statistics. At ILDA, we are working with Dutch-based international development organisation HIVOS to explore the data on violence against the LGBTQ+ community in Central America. It is evident that, without data on gender, sexual orientation or other variables, it is very difficult to understand how serious the problem is becoming in that region (or in any other, for that matter).

Whose perspective is the default in society and in our data?

In their book, D’Ignazio and Klein draw attention to the fact that the default perspective hides privilege. We all have biases and experience their impact in our daily lives. They can be related to gender, race, age and class, among others, and can result in different types of discrimination. These positions of power, however, end up transmitting into the data. Power becomes part of the processes by which data is produced and, in many cases, in the standards that guide its production (I have argued, together with Michael Cañares and Brandusescu, that bias can creep into open data standards). Failing to realise how power determines data can make some populations invisible in datasets, algorithms, and visualisations, to name just a few examples.

How we produce and consume data affects people’s lives

This discussion may seem quite technical, but it has an impact on all of our lives, especially on those of us who are most disadvantaged. A statistical pattern that applies to the majority may not be applicable to a minority group. In this respect, bias affects people's lives, when making decisions and when they are the subject of the decision-making processes of others.

We, as a society, must be aware of the implications of the data we produce and consume. It affects the information we take in about politics, our understanding of which benefits we are entitled to, and whether certain opportunities arise just because we belong to a certain demographic. We are still learning to deal with and mitigate prejudice. There is a long way to go, but the first step is becoming more aware of these dangers and their implications. — Silvana Fumega

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