1. We refuse to operate under the assumption that risk and harm associated with data practices can be bounded to mean the same thing for everyone, everywhere, at every time. We commit to acknowledging how historical and systemic patterns of violence and exploitation produce differential vulnerabilities for communities.
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. I
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
Data Feminism
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to d...
Data Refusal from Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design
Amidst calls for public accountability over large data-driven systems, feminist and indigenous scholars have developed refusal as a practice that challenges the authority of data collectors. However, because data affect so many aspects of daily life, it can be hard to see seemingly different refusal strategies as part of the same repertoire. Furthermore, conversations about refusal often happen from the standpoint of designers and policymakers rather than the people and communities most affected by data collection. In this article, we introduce a framework for data refusal from below —writing from the standpoint of people who refuse, rather than the institutions that seek their compliance. Because refusers work to reshape socio-technical systems, we argue that refusal is an act of design and that design-based frameworks and methods can contribute to refusal. We characterize refusal strategies across four constituent facets common to all refusal, whatever strategies are used: autonomy , or how refusal accounts for individual and collective interests; time , or whether refusal reacts to past harm or proactively prevents future harm; power , or the extent to which refusal makes change possible; and cost , or whether or not refusal can reduce or redistribute penalties experienced by refusers. We illustrate each facet by drawing on cases of people and collectives that have refused data systems. Together, the four facets of our framework are designed to help scholars and activists describe, evaluate, and imagine new forms of refusal.
Data Cívica | Organización feminista que utiliza los datos y la tecnología como herramientas para la defensa de los derechos humanos y la justicia en México.
Más datos para más personas
In the face of rampant AI, is ‘data poisoning’ a new form of civil disobedience?
Boycotts, sabotage and other types of civil disobedience have long served collective action against injustice.
In the face of rampant AI, is ‘data poisoning’ a new form of civil disobedience?
Boycotts, sabotage and other types of civil disobedience have long served collective action against injustice.
MyData
The human-centric approach to data is aimed at a fair, sustainable, and prosperous digital society. In such a society, people get value from their data and set the agenda on how it is used. And for organisations, the ethical use of data is always the most attractive option.
Movement Data Science: Practices for Working in Solidarity with Social Justice Efforts
Although many data scientists are eager to promote social justice, their efforts often fall short, and in some cases even exacerbate existing injustices. This gap between intentions and impacts follows from issues in how data scientists conceptualize their role in social justice efforts: they typically act as technicians, aiming to develop technical tools that will produce social justice. This default role generally results in depoliticized techno-fixes that fail to produce substantive changes. In this paper, we contribute “movement data science” as a framework to guide data scientists in taking on more productive and equitable roles in social justice efforts. Our framework is inspired by movement lawyering, a practice used by lawyers to situate themselves in grassroots social movements. Movement data science suggests that data scientists should ground their efforts to advance justice in sustained and solidaristic relationships with social movements. We contribute a process with four essential practices to help data scientists build these relationships and identify roles to play within specific social justice efforts. Movement data science does not provide a simple blueprint for data scientists to follow, but it can help them support social justice movements in substantive ways that go beyond attempting to solve injustices with technical tools.
764: The Intersection of Terrorism, Violent Extremism, and Child Sexual Exploitation
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Living in Data: A Citizen's Guide to a Better Information Future (Paperback)
Jer Thorp’s analysis of the word “data” in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with “data,” we find not only its classic companions “information” and “digital,” but also a variety of new neighbors—from “scandal” and “misinformation” to “ethics,” “friends,” and “play.”To live in data in the twenty-first century
History of Anarchaserver and Feminists Servers visit this section - Anarchaserver
Trigger Warning: This text and the accompanying images contain references to medical violence, slavery, and non-consensual experimentation. Reader discretion is advised.
AI-powered online abuse: How AI is amplifying violence against women and what can stop it | UN Women – Headquarters
AI is turning online spaces into new frontiers of abuse. Feminist and tech experts, Laura Bates and Paola Gálvez-Callirgos, explain how it happens and how we fight back.
Invisible Women: Exposing Data Bias in a World Designed for Men
Invisible Women: Exposing Data Bias in a World Designed for Men is a 2019 book by British feminist author Caroline Criado Perez. The book describes the adverse effects on women caused by gender bias in big data collection.
How AI is exacerbating technology-facilitated violence against women and girls
This paper examines how artificial intelligence is accelerating technology-facilitated violence against women and girls—from deepfakes and automated hate to sextortion, impersonation, and large-scale disinformation. It outlines emerging risks, legal and ethical challenges, and opportunities for prevention, while calling for urgent regulation, safety-by-design, and coordinated global action to ensure that AI technologies advance, rather than undermine, women’s rights and safety.
Can data collectives help strengthen vulnerable cultures in the face of AI?
"Data collectives and cooperatives, which let creators control the collection and distribution of their data, are emerging as preferred alternatives to big tech companies."
