One of the first questions that may come to mind upon seeing the name of this book is how "Feminism" and "Data" are related to each other and what it means when they sit together in the title "Data Feminism". In the following text, I will try to summarize the meaning of this phrase by referring to some text from the book. But before that, I would like to explain why I have chosen this book for translation. My first reason is that the book attempts to make the voices of marginalized people heard. People who are marginalized are often overlooked by data science, or even more troubling, their existence is acknowledged but disregarded, which contributes to their oppression by data-driven systems. Another reason for choosing this book is that the authors are reflexive and transparent by questioning their situations as two privileged white professors in academic positions. They took a stand against a world that has created conditions in their favor but at the expense of others, and maintaining their position has not prevented them from criticizing the power structures. In other words, they pulled the rug from under their own feet, and by scrutinizing their privileges, they prompted serious questions about the power structure in data science. I hope reading this book gives you a new perspective on data and data science with a critical approach. Finally, with the thought that every piece of knowledge produced is the result of the collective efforts of numerous individuals, many of whom going unnoticed, and with the belief that this generated knowledge belongs to everyone and should be accessible to all, I provide the translation of this book for free on the web.

But, what is Data Feminism? ‌Data Feminism is not just about women, it is not just for women, and it is not just about gender. Data Feminism is for all those who work with data in some way and want to learn how to think critically about data and data science and work toward justice. Data Feminism is also for activists curious to understand how data and data science affect their social activities. The ultimate goal of Data Feminism can be summed up as: "co-liberation", the liberation of human beings from the structures and relationships that have restrained them and made them "alienated". This sense of alienation is a direct result of a system that prioritizes the generation of "profit" as its foremost value. Within this system, both the people in power and the oppressed are caught in the cycle of profit generation.

Escaping alienation will not be achieved without a concerted effort toward co-liberation, and the key to this liberation is the commitment and belief in mutual benefit, both from the dominant groups and the oppressed. This is the "co" in co-liberation. In this regard, Marx reminded us how the liberation of capitalists is tied to the liberation of workers, and Tawana Petty, an anti-racist activist, said: "We need whites to firmly believe that their liberation, their humanity, is also dependent upon the destruction of racism and the dismantling of white supremacy". Men also need to understand how unequal gender relations in the institutions they dominate harm everyone, including themselves, and addressing those inequalities is beneficial for everyone. I also very much like a quote by aboriginal activists in Queensland, Australia, in the 1970s, which says: "If you have come here to help me, you are wasting your time. But if you have come because your liberation is bound up with mine, then let us work together".

To break the chains and move towards co-liberation, the starting point of Data Feminism is to acknowledge that in today's world, "data" and data-driven systems are "power", and this power is not equally available to everyone. Owners of data and systems are often elite, straight, white, able-bodied, cisgender men from the Global North. Data Feminism aims to recognize the practices of data science that reinforce these injustices and use the same data science to challenge and change those practices. It is important to point out that Data Feminism is a form of "intersectional feminism" that reminds us that each person's experience and opportunities in the world are the result of the intersection of various aspects of that person's identity, such as race, class, gender, sex, ability, age, religion, geography and more. Understanding this intersectional perspective is critical in recognizing the injustices arising from data science.

Data Feminism also points to the fact that data and data-driven systems are not neutral and objective, and it is difficult to detect forces and exploitations of domination that benefit from them. Here, asking "who" questions can help us identify those forces and exploitations: Who does collect the data? Whose data is collected? Who does benefit from that data? Whose preferences become data products? Whose needs are being ignored? And many other questions. The purpose of raising these questions is not only to understand the forces of domination and recognize biases but also to challenge and change the relations that construct and strengthen them. A common approach to dealing with injustices arising from data science is to relate them to individual or systemic mistakes. Although there are certainly individual and systemic mistakes, and they are important, Data Feminism believes that domination, oppression, and exploiters hidden in data and data science have historical and social roots, and in order to understand and solve those problems, one must search for their reasons in history, social relations, and power relations. This understanding leads to rethinking and restructuring of data science. Proposing a solution without considering these roots leads us to wrong solutions at worst or maintains the current situation at best.

According to these points, Fata Feminism considers its mission to use data science to solve the mentioned shortcomings in data science. For this purpose, it has proposed seven basic principles:

Examine Power

Data Feminism begins by examining how power works in the world by posing and asking the "who" questions about data science. Asking these questions helps us to understand how the unequal distribution of power in different subjects and the results it brings about.

Challenge Power

Data Feminism is committed to criticizing and challenging unequal power structures and working toward justice. To this end, it is necessary to go beyond the passive approaches that consider the origin of problems in individuals and systems, which would either lead to wrong solutions or maintain the current situation. On the contrary, Fata Feminism recommends moving towards proactive approaches that work to resolve structural power inequalities by understanding the root causes.

Elevate Emotion and Embodiment

Data Feminism teaches us to value different forms of knowledge, including knowledge from people as living and feeling bodies. Emotions, affect, and embodiment are aspects of the human experience often associated with women and are usually dismissed based on male-dominant stereotypical logic. This patriarchal approach excludes many aspects of the human experience in favor of those in power, and Data Feminism is committed to changing this perspective.

Rethink Binaries and Hierarchies

Counting and classification are important parts of the knowledge generation process and, therefore, tools of power. Historically, counting and classification have been used to dominate, discipline, and exclude. Among the first examples of these classifications, we can mention the binary gender category. Data Feminism requires us to challenge binaries and systems of counting and classifications that perpetuate oppression.

Embrace Pluralism

Data Feminism emphasizes that the most complete form of knowledge is obtained by combining different perspectives and prioritizing local, indigenous, and experimental knowledge methods. One of the dangers of working with data for data scientists is that they are usually strangers to the context of that data. Embracing pluralism is a feminist approach to reducing this risk by providing a platform for transferring knowledge and experience from communities close to data to experts and vice versa.

Consider Context

Data Feminism believes that data is not neutral. They are the product of unequal social and power relations, and it is necessary to consider the context of the data to conduct their accurate and ethical analysis. Context means understanding the conditions and environment in which the data is collected. Data Feminism warns us to be careful when dealing with data by considering these questions: What power disparities contribute to gaps or the complete absence of data within the dataset? Who faces conflicts of interest that interfere with full transparency in sharing their data? Whose insights into a particular issue have been suppressed, and how can we recover that knowledge?

Make Labor Visible

The work of data science, like all work in the world, is the result of the efforts of many people. Data Feminism makes these works visible so that they can be recognized and valued. When faced with any data and system, one should pay attention to who is credited and whose work is ignored. Data Feminism uses data science to reveal the work of people (mainly women) who do the work of social reproduction: emotional work, care work, and domestic work. Making labor visible is essential to ensure credit is given to invisible and undervalued work.

In the end, quoting the authors, Data Feminism, like justice, is both a goal and a process that guides us while moving towards the goal, which is co-liberation and building a better world. Problems and difficulties are inevitable parts of this movement, and to deal with them, we need to join forces and walk this path with sympathy. So now let's multiply. Let's multiply now.