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Digital technology and algorithms have revolutionised the way we live, work and communicate. While promising efficiency, accuracy and convenience, these technologies can exacerbate prejudice and social inequalities exacerbate prejudice and social inequalities and create new forms of exclusion and create new forms of exclusion. Thus, invisibilisation and discrimination, which have always existed, take on new forms in the age of algorithms.

Lack of interest and data leads to algorithmic invisibilisation, leading to two types of algorithmic neglect. The first of these is among the world's underserved, which includes the millions who do not have a smartphone or a bank account millions who do not have a smartphone or a bank account, and are thus on the margins of the platform economy and who are therefore on the margins of the platform economy and, for algorithms, do not exist. The second type of algorithmic abandonment includes individuals or groups who are victims of the failure of the algorithmic system, as was the case with SyRI(Systeem Risico Indicatie)SyRI(Systeem Risico Indicatie) in the Netherlands that unfairly singled out some 20,000 families from low socio-economic backgrounds for tax fraud, leading many to ruin by 2021. The algorithm, which the algorithm, which was declared illegal by a court in The Hague months later, was applied in the country's poorest neighbourhoodsthe algorithm, which was declared illegal by a court in The Hague months later, was applied in the country's poorest neighbourhoods the algorithm, which was declared illegal by a court in The Hague months later, was applied in the poorest neighbourhoods of the country and blocked many families with more than one nationality from receiving the social benefits to which they were entitled because of their socio-economic status.

Beyond the example in the Dutch public system, invisibilisation and discrimination can also originate in the private sector. One example is Amazon's amazon's job posting algorithm which showed a bias against women by learning from historical data - i.e. incomplete data because it did not include a large and representative universe - leading Amazon to abandon the project. which showed a bias against women by learning from historical data - i.e. incomplete data because it did not include a large and representative universe - leading Amazon to abandon the project. Another example is Apple Card, a credit card backed by Goldman Sachs, which was also singled out when its algorithm was found to offer more favourable credit limits to men than to women.

In general, invisibility and algorithmic discrimination, in any field, can lead to unequal access to resources and exacerbate social and economic exclusion.

Making decisions based on algorithms

Data and algorithms are interconnected components in computing and information processing. Data serve as a basis, but can be unstructured, with excessive variability and incompleteness. Algorithms are instructions or procedures designed to process and structure this data and extract meaningful information, patterns or results.

The quality and relevance of the data directly impacts the effectiveness of the algorithms, as they rely on the data inputs to generate results. Hence, the principle "rubbish in, rubbish out"which summarises the idea that if poor quality, biased or inaccurate data enters a system or process, the result will also be of poor quality or inaccurate. On the other hand, well-designed well-designed algorithms can enhance the value of data by revealing hidden relationships or making by revealing hidden relationships or making predictions.

This symbiotic relationship underscores the critical role that both data and algorithms play in driving technological advances, enabling informed decision making, and This symbiotic relationship underscores the critical role that both data and algorithms play in driving technological advances, enabling informed decision making, and fostering innovation.

Algorithmic decision making refers to the process of using predefined sets of instructions or rules to analyse data and make predictions to aid decision making. Increasingly, it is being applied to decisions that have to do with social welfare social welfare and the provision of commercial services and products through platforms. This is where invisibility or algorithmic discrimination can be found.

Increasingly, welfare systems are using data and algorithms to help make decisions on issues such as who should receive what kind of care and who is at risk. These algorithms consider different factors such as income, family or household size, expenditures, risk factors, age, sex or gender, which may include biases and omissions.

That is why the That is why the Special Rapporteur on extreme poverty and human rights, Philip Alston, warned in a report to the UN General Assembly that the uncautious adoption of these can lead to dystopian social welfare dystopian social welfare. In such a dystopian welfarestate , algorithms are used to reduce budgets, reduce the number of beneficiaries, eliminate services, introduce demanding and intrusive forms of conditionality, modify behaviour, impose sanctions and "reverse the notion that the state is accountable".

Algorithmic invisibility and discrimination: Two opposing concepts

Although data and algorithms have much in common, algorithmic invisibility and discrimination are two opposing concepts. Algorithmic invisibility refers to gaps in data sets or omissions in algorithms, which result in inattentions in the application of benefits or services. In contrast, algorithmic discrimination speaks to hotspots that highlight specific communities or biased characteristics in datasets, generating unfairness.

That is, algorithmic invisibilisation occurs when individuals or groups are absent from datasets, making it impossible to address their needs. For example, integrating data on women with disabilities into social decision-making can be vital for the inclusion of women with disabilities in society. Globally, women are more vulnerable to algorithmic invisibilisation than men, as they have less access to digital technology have less access to digital technology and leave fewer digital traces.

Opaque algorithmic systems that incorporate stereotypes can increase invisibilisation and discrimination by hiding or targeting vulnerable individuals or populations. An opaque algorithmic system is one that does not allow access to its operation.

On the other hand, aggregating or disaggregating data without careful consideration of the consequences can result in omissions or errors result in omissions or errors. This illustrates the double-edged nature of accounting; that is, the ambivalence of technology that quantifies and counts, and that can serve to improve people's lives, but also to harm them.

Discrimination can arise when algorithmic decisions are based on historical data, which usually incorporate asymmetries, stereotypes and injustices, because more inequalities existed in the past. The "rubbish in, rubbish out" effect occurs if the data is skewed, as is often the case with online content. Also, biased or incomplete databases can be incentives for algorithmic discrimination. Selection biases may arise when facial recognition data, for example, is based on the features of white men, while the users are dark-skinned women, or on online content generated by a minority of agentswhich makes generalisation difficult.

As can be seen, tackling invisibility and algorithmic discrimination is a major challenge that can only be solved through awareness-raising and collaboration between institutions, campaigning organisations, businesses and research.

Content prepared by Miren Gutiérrez, PhD and researcher at the University of Deusto, expert in data activism, data justice, data literacy and gender disinformation.

The contents and views reflected in this publication are the sole responsibility of the author.

Effective equality between men and women is a common goal to be achieved as a society. This is stated by the United Nations (UN), which includes "Achieve gender equality and empower all women and girls" as one of the Sustainable Development Goals to be achieved by 2030.

For this, it is essential to have quality data that show us the reality and the situations of risk and vulnerability that women face. This is the only way to design effective policies that are more equitable and informed, in areas such as violence against women or the fight to break glass ceilings. This has led to an increasing number of organisations opening up data related to gender inequality. However, according to the UN itself, less than half of the data needed to monitor gender inequality is currently available.

What data are needed?

In order to understand the real situation of women and girls in the world, it is necessary to systematically include a gender analysis in all stages of the production of statistics. This implies from using gender-sensitive concepts to broadening the sources of information in order to highlight phenomena that are currently not being measured.

Gender data does not only refer to sex-disaggregated data. Data also need to be based on concepts and definitions that adequately reflect the diversity of women and men, capturing all aspects of their lives and especially those areas that are most susceptible to inequalities. In addition, data collection methods need to take into account stereotypes and social and cultural factors that may induce gender bias in the data.

Resources for gender mainstreaming in data

From datos.gob.es we have already addressed this issue in other contents, providing some initial clues on the creation of datasets with a gender perspective, but more and more organisations are becoming involved in this area, producing materials that can help to alleviate this issue.

The UN Statistics Division produced the report “Integrating a Gender Perspective into Statistics” to provide the methodological and analytical information needed to improve the availability, quality and use of gender statistics.  The report focuses on 10 themes: education; work; poverty; environment; food security; power and decision-making; population, households and families; health; migration, displaced persons and refugees; and violence against women. For each theme, the report details the gender issues to be addressed, the data needed to address them, data sources to be considered, and specific conceptual and measurement issues. The report also discusses in a cross-cutting manner how to generate surveys, conduct data analysis or generate appropriate visualisations.

UN agencies are also working on this issue in their various areas of action. For example, Unicef has also developed guides of interest such as “Gender statistics and administrative data systems”, which compiles resources such as conceptual and strategic frameworks, practical tools and use cases, among others.

Another example is the World Bank. This organisation has a gender-sensitive data portal, where it offers indicators and statistics on various aspects such as health, education, violence or employment. The data can be downloaded in CSV or Excel, but it is also displayed through narratives and visualisations, which make it easier to understand. In addition, they can be accessed through an API.  This portal also includes a section where tools and guidelines are compiled to improve data collection, use and dissemination of gender statistics. These materials are focused on specific sectors, such as agri-food or domestic work. It also has a section on courses, where we can find, among others, training on how to communicate and use gender statistics.

Initiatives in Spain

If we focus on our country, we also find very interesting initiatives. We have already talked about GenderDataLab.org, a repository of open data with a gender perspective. Its website also includes guides on how to generate and share these datasets. If you want to know more about this project, we invite you to watch this interview with Thais Ruiz de Alda, founder and CEO of Digital Fems, one of the entities behind this initiative.

In addition, an increasing number of agencies are implementing mechanisms to publish gender-sensitive datasets. The Government of the Canary Islands has created the web tool “Canary Islands in perspective” to bring together different statistical sources and provide a scorecard with data disaggregated by sex, which is continuously updated. Another project worth mentioning is the “Women and Men in the Canary Islands” website, the result of a statistical operation designed by the Canary Islands Statistics Institute (ISTAC) in collaboration with the Canary Islands Institute for Equality. It compiles information from different statistical operations and analyses it from a gender perspective.

The Government of Catalonia has also included this issue in its Government Plan. In the report "Prioritisation of open data relating to gender inequality for the Government of Catalonia" they compile bibliography and local and international experiences that can serve as inspiration for both the publication and use of this type of datasets. The report also proposes a series of indicators to be taken into account and details some datasets that need to be opened up.

These are just a few examples that show the commitment of civil associations and public bodies in this area. A field we must continue to work in order to get the necessary data to be able to assess the real situation of women in the world and thus design political solutions that will enable a fairer world for all.

Today, data drives the world. It conditions public policies, the behaviour of algorithms and the decision-making of many companies. That is why it is important to have figures that correctly represent reality, i.e. that take into account all variables, including gender.

Thais Ruiz de Alda is founder and CEO of Digital Fems, an entity that designs projects to increase the presence of women in technological environments. In addition to consultancy tasks and the design of equality policies, Digital Fems carries out projects based on data science with a gender perspective. In this interview, Thais talks about the current situation and the challenges in this field (video only available in Spanish).

Full interview:

1. Why is it important to have gender-sensitive data?

Gender-sensitive data is a tool to measure various aspects, in a differentiated way between men and women, between different sexes.

It also serves to measure reality in terms of gender identity, if information is available.  Finally, there are subjects, areas of data processing, where it is essential to include intersectional perspectives, such as gender or origin. For example, when data is collected on the use of health services, we can see differentiated effects depending on whether the patients are of different sexes, or for example in the use of public transport, it is important to identify the sex of the person using the service, in order to design a service in accordance with the needs of the passengers: space for breastfeeding, space for carrying children, safety and avoidance of sexual harassment or other types of aggression. The problem we still have today is the lack or non-existence of this type of data. The famous gender data gap. That is why many organisations say that without data with a gender perspective, equality is not possible. Without GenderData , equality is not.

2. What is the current status of this type of data? You indicate that there is a gender gap in the data…

There is a tremendous gender gap in data. In general, and since the era of open government began in 2007, public administrations have been the ones that started opening data. This makes sense, given that administrations have been the generators of official statistics and the owners and guardians of some of the data that citizens create through the use of public services. According to the United Nations, by December 2020, we had only 39% of the gender-sensitive data we need to monitor the SDGs. So, from the public authorities' side, we still have some way to go. I think the future outlook for this kind of data is positively good, because we are on the right track, making progress in creating this kind of data. This is where we can say that, in parallel, there are civil society organisations that are also working on the generation of gender-sensitive data. Many women's organisations have realised the need to create and collect this data in order to alleviate this gap. In fact, right now, civil society organisations are the ones that should be pushing and lobbying to show the value of this type of data and pushing for public authorities to generate it. Now we need other stakeholders such as companies or academic environments to prioritise this need and generate data with a gender perspective in order to understand issues that affect men and women differently. The topic could be the subject of a doctoral thesis... but in short, the first stumbling block to be resolved is to produce data with this perspective, which has been ignored, and once we have these data, we will be able to read reality, measure it and draw conclusions that will allow us to make decisions with much greater precision.

According to the United Nations, by December 2020, we had only 39% of the gender-sensitive data we need to monitor the SDGs

3. Digital Fems, together with other organisations, has set up GenderDataLab.org, a repository of open data with a gender perspective. What kind of information can users find there? What are the challenges you have encountered in collecting and making this kind of data public?

Genderdatalab is a recently created space for experimentation and publication of datasets with a gender perspective, where visitors can choose to:

• Learn through articles, recommendations, guides or best practices and information collected on the discipline of data with a gender perspective. It is a space of common use because after registering, users can create datasets and publish them, with open licenses to publish their study reports, etc.
• Register and publish datasets; it is a space of common use because after registering, users can create and publish datasets, with open licenses to publish their study reports, etc.
• Download or use the API of the datasets, or simply visit the datasets and visualise them... 

Despite our "youth" we have had diverse experiences: we have convinced organisations to publish their data, which contained the gender perspective, in open format and they have had some fears that open data is susceptible to manipulation. Therefore, we have evangelised about open data to non-digitised communities. On the other hand, we have seen how, on the contrary, organisations that wanted to publish gender-inclusive reports, opened up to do so, and asked us for help and support in implementation. We have also detected some fears in the use of the platform, i.e. resistance to publish datasets for "fear" that they are not well designed, etc. and that is why we are now going to publish mini training courses to familiarise users with the functionalities of the platform, as well as with the contents and encourage the members of the platform, which already has a few hundred people registered.

4. One of the areas where data can also help us is in the fight against gender violence. This is the field of work of your project DatosContraelRuido.org, where you use Big Data techniques to analyse thousands of data files on the subject. How have you developed the project and what has been its impact?

The DatosContraElRuido.org project was the first project that we launched at Digitalfems in terms of gender data activism. We developed the project so that, through the application of our methodology, complex legal concepts could be understood in data visualisations, processed and analysed from a gender perspective, which could explain the presence of male violence in Spain, or the typology of violence that is exercised with the data that the Ministry of Justice and the General Council of the Judiciary publish.  With all these thousands of lines of information, we have been able to create an understandable story for ordinary people, and to design communication campaigns that allow us to understand the dimension of male violence.

Each time we publish an update of the data, we achieve a relatively important media impact, which has allowed us to be invited to many forums, especially in the context of male violence, to explain three issues:

1. Creating technology or technological solutions with a gender perspective helps to broaden the field of vision of the problems. We need more women technologists who can address social issues.
2. GenderData is a discipline of data science that is not only concerned with data collection, but also applies to the way data is structured and processed for analysis. 
3. All data can be downloaded from GenderDataLab.org so that anyone can in turn process the data and expand the scope of analysis.

The social impact we are aiming for is to clarify the high prevalence of male violence, based on official, undeniable data.... and to raise social awareness about it. For us, DatosContraelRuido.org is an open and accessible tool for society to know the reality of a type of violence that needs to be spoken out loud and clear. If drugs, traffic accidents and public safety are areas of public interest, so too is the violence that some men inflict on women. Seventy per cent of complaints are filed away...

DatosContraelRuido makes it possible to understand complex legal concepts in data visualisations, processed and analysed from a gender perspective. The aim is to explain the presence of male violence in Spain, or the typology of violence that is exercised with the data published by the Ministry of Justice and the General Council of the Judiciary.

5. In your opinion, what should be the strategic actions to generate gender-sensitive data from an institutional perspective?

We live in data-driven societies, and we are getting more and more... so it would make perfect sense to take into account the different tools, methodologies and processes that help to generate the best possible quality data. Here it is very clear that we need an action plan to make this possible.

First and foremost, training must be provided to individuals, departments and teams responsible for maintaining datasets or with the potential to create datasets within the public administration. It is necessary to invest in training the people who manage data generation. In fact, it is a "leg" of what is meant by digitising or digitally transforming public administration.

The second is to promote the creation of this type of data through administrative instruments. For example, the European Commission announced in 2020 that beneficiaries of its research grants would have to incorporate sex and gender analysis in the design of their studies, probably due to the experience of COVID-19 and vaccines.

The third is to raise awareness of this new discipline, and the benefits it would bring, but this without the other actions is useless. And most importantly, without budgets to incentivise change or put in place elements of innovation, we do nothing....

First and foremost, training must be provided to individuals, departments and teams responsible for maintaining datasets or with the potential to create datasets within the public administration.

6. Although it is a sector in constant growth, women are still a minority in work environments linked to the technological field. What are the reasons behind this situation? What measures should be taken to change it?

It is complex because this reality is found all over the planet, countries and territories. One of the strongest reasons is that there is a strong presence of gender stereotypes about "technology". There are many, many studies that show how even from an early age, girls and boys associate technology with masculine skills. Let's be aware that in the cradle of tech culture, Silicon Valley, there is a term that is constantly used to define traits of corporate cultures: Brogrammer, a fusion between brother and programmer.

Stereotypes operate invisibly, and are one of the reasons why there are no women university students in specific engineering-related fields, and therefore there are also  low rates into professional environments. It is said that women represent approximately 30% of the total number of employees in the tech sector, in a sector whose growth rate is 10% per year, vs. 0.4%, which is the rate of growth of the employment rate in the Eurozone. So the recruitment rate of female technologists is low because there are few of them, but this rate continues to fall as careers develop and the retention of female talent is an unresolved issue in the tech sector.

The solution to this is complex, because it implies that on the one hand, public policies must be activated to generate actions that promote a greater female presence. For example, Barcelona City Council has been a pioneer in regulating and setting criteria and means to change the trend of the sector (the government measure is called BcnFemTech). On the other hand, corporate policies must also and above all be activated among the companies that form part of the sector through the creation of measures that encourage the entry of more women, and the retention of this talent, which also has a direct impact on the company's profits: the more diverse people who design software, the better and more effective it will be, as the Bill and Melinda Gates Foundation says.

The recruitment rate of female technologists is low because there are few of them, but it is also because this rate continues to fall as careers develop and the retention of female talent is a pending issue in the tech sector.

7. Can you tell us about Digital Fems' next lines of work in the field of open data?

Well, we continue to work with data from some of the organisations we collaborate with, for example with CIMA, where we follow up on their reports on the presence of women in film, and we monitor the evolution of the number of women working in the industry, directing films or scriptwriting them, and we calculate the gender gap. We are also going to publish openly two works we have done this year: a survey of companies based in Catalonia about women's roles and tasks in technological environments, and a report and dataset about women in tech environments in Spain. We are very happy because these two reports will shed light on the reality of women technologists in Spain. By the last quarter of 2022 we will probably be working on a data and music project as well, through EllesMusic: the music sector works with non-standardised metadata, and gender should be incorporated as an element of metadata.

Today, 8 March is the day on which we commemorate women's struggle to achieve their full participation in society, as well as giving visibility to the current gender inequality and demanding global action for effective equality of rights in all areas.

However, the data seem to indicate that we still have some way to go in this respect. 70% of the 1.3 billion people living in poverty are women. Women predominate in global food production (up to 80% in some areas), but own less than 10% of the land. Eighty per cent of people displaced by disasters and climate-related changes worldwide are women and girls. And the situation for women has only worsened due to the pandemic, causing the estimate of the time needed to close the current gender gap to now grow to more than 135 years.

The importance of data in the fight for equality

It is therefore a fact that women have fallen behind on many of the sustainable development indicators, an inequality that is also being replicated in the digital world - and even amplified through the increasing use of algorithms that lack the necessary training data to be representative of women's reality. But it is also a fact that we do not even have all the data we need to know with certainty where we stand on a large number of key indicators.

There is a widespread shortage of gender data that cuts across all economic and social sectors. The World Bank, the European Union, the OECD, the United Nations, UNICEF, the ITU or the IMF - more and more international bodies are making their own particular efforts to compile their own gender databases. However, indicators are still lacking in many key areas, in addition to other important gaps in the quality of existing data that are often incomplete or outdated.

This lack of data is something that can be particularly problematic when it comes to such sensitive issues as gender-based violence - an area where we are fortunately seeing more and more data globally, including some great and encouraging examples such as the ILDA-led femicide data initiative. This is a very important step forward because it is even more difficult to improve when we don't even know what the current situation is. Data, and the governance policies we create to manage it, can also be sexist.

Data are tools for making better decisions and better policies. They allow us to set goals and measure our progress. Data has therefore become an indispensable tool for creating social impact in communities. This is why the lack of data on the lives of women and girls is so damaging.

Addressing the gender gap through data

In seeking solutions to this problem, and thus working for gender equality also through data, it is crucial that we involve the protagonists and give them a voice. In this way, through their own experiences, we can develop more inclusive processes for data collection, analysis and publication. We will then be in a much better position to use data as an inclusive tool to address gender equality. Catherine D'Ignazio and Lauren Klein's excellent Data Feminism Handbook provides a set of strategies and principles to guide us in doing this:

1. Examining power - Data feminism begins by looking at how power operates in the world.
2. Challenging power - We must commit to challenging power structures when they are unequal and working for equity.
3. Empowering emotions and embodiment - Data feminism teaches us to value multiple forms of knowledge, including that which comes from people.
4. Rethink binarisms and hierarchies - We must challenge gender binarism, as well as other systems of quantification and classification that could lead to various forms of marginalisation.
5. Embrace pluralism - The most complete knowledge emerges from synthesising multiple perspectives, prioritising local knowledge and experiences.
6. Consider context - Data are neither neutral nor objective. They are products of unequal social relations, and understanding that context will be essential to ethical and accurate analysis.
7. Make the work visible - The work of data science is the collaborative product of many people. All of this work must be made visible, so that it can be recognised and valued.

Nuestras opciones para contribuir a reducir la brecha de datos

In order to make progress in this fight for equality, we need much more gender-disaggregated data that adequately reflects the concerns of women and girls, their diversity and all aspects of their lives. We can and should all do our part in drawing attention to the disadvantages women face through data. Here are some tips:

• Start by always collecting and publishing data disaggregated by gender.
• Always use women as a reference group in our calculations when we are dealing with inequalities that affect them directly.
• Document the decisions we make and our methodologies in working with gender data, including any changes in our approaches over time and their justification.
• Always share raw and complete data in an open and reusable format. In this way, even if we have not focused on the challenges women face, at least others can do so using the same data.

Together we can make the invisible visible and finally ensure that every single woman and girl in the world is counted. The situation is urgent and now is the time to make a determined bid to close the data gap as a necessary tool to close the gender gap as well.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation. The contents and views expressed in this publication are the sole responsibility of the author.

More than half of the world's population are women, who also play a key role in our society. For example, it is women who grow, produce and sell more than 90% of locally grown food. Paradoxically, these same women are beneficiaries of only 1% of agricultural loans and receive less than 1% of public contracts. One of the reasons for this growing discrimination is precisely the scarcity of the availability of the gender data required to adequately evaluate public policies and ensure that women are included and their particular needs taken into account.

As we see, far from taking advantage of the benefits promised by open data and appart from suffering the usual discrimination due to gender issues, women around the world are now also forced to live a new form of discrimination through the data: women have less online presence than men; they are generally less likely to be heard in the consultation and design phase of data policies; they are less valued in the rankings of data scientists and usually they do not even have representation in official statistics.

The goals defined through the Sustainable Development Goals include a specific objective to eliminate all forms of discrimination against women. However, even though we already have a great variety of data disaggregated by sex, a recent study by the United Nations has detected the existence of  important gender data gaps when dealing with these specific sources of discrimination in such relevant areas such as health, education, economic opportunities, political participation or even one's physical integrity.

Ending discrimination will be a much more difficult task if you do not even have the basic data necessary to understand the extent of the problem to solve it. Therefore, an important first step is to make the most of the already available data, but also be able to clearly visualize these deficiencies. Political commitment at the highest level is very high with initiatives such as the Global Data Alliance for Sustainable Development, the Open Data Charter or the African Consensus on Data, showing their explicit support for more inclusive data policies. Nevertheless, this commitment has not materialized, as even today only 13% of governments include in their budgets the regular collection of gender data.

In order to close this new digital gender gap, a new comprehensive approach will therefore be necessary to identify the necessary data, ensure that this data is collected and shared as open data, conduct training actions so the interested parties can understand and analyze these data by themselves and enable dialogue and participation mechanisms to ensure that public budgets adequately capture these needs.

In an increasingly digital world, without equality of data, we will not be able to understand the totality of the reality about women's life and well-being, nor reach true gender equality to make each and every one of women  be taken into account.