Catherine D’Ignazio, Counting Feminicide: Data Feminism in Action, Cambridge, MA: MIT Press, 2024, 392 pp., $34.95 (hardcover).

Reviewed by

Muge Yuce

George Mason University

 

In Counting Feminicide: Data Feminism in Action, Catherine D’Ignazio shifts the terrain of data science by treating it not as a neutral technical instrument but as a situated, relational, and political practice. Grounded in feminist, decolonial, and critical data studies, the book asks what data does in the world: whom it serves, whom it harms, and how it might be made accountable to communities living with structural violence. Drawing on a multi-year collaboration with grassroots feminicide data activists across Latin America and the United States, D’Ignazio develops what she calls restorative/transformative data science: an approach oriented away from prediction, scale, and optimization and toward care, memory, dignity, justice, and structural change.

 

The book emerges in a context marked by two converging developments. First is the global expansion of feminicide—the gender-related killing of women and gender-diverse people—alongside the persistent failure of states to adequately monitor, prevent, or respond to this violence. Second is the growing political authority of data, algorithms, and artificial intelligence in shaping public policy, resource distribution, and public knowledge. What matters here is that they reinforce one another: Data systems claim neutrality and objectivity while reproducing racialized, gendered, and colonial forms of harm.

 

Against this backdrop, feminicide data activists have developed practices of counting, documenting, mapping, and circulating information as forms of political intervention. D’Ignazio shows how activists use “counterdata” to challenge missing or biased official statistics, reframe feminicide as a structural problem rather than a series of isolated crimes, and mobilize publics, media, and policy actors. In this sense, data activism does not merely fill gaps in official data but contests the epistemic and political authority that determines whose deaths are recognized, how they are classified, and whether they matter at all. D’Ignazio persuasively frames data activism as a form of community defense that refuses erasure, honors lives, supports families, and demands accountability from states that systematically undercount, misclassify, or ignore gendered and racialized violence.

 

Read alongside feminist and critical data studies, especially D’Ignazio and Lauren Klein’s (2020) Data Feminism, the book marks an important shift from the existing scholarship, which criticizes the harms of dominant technological systems, toward an engagement with practices already being built otherwise. In other words, rather than remaining at the level of inquiring how data can be made more ethical or inclusive within existing institutions, D’Ignazio asks what it would mean to build data practices from the standpoint of communities harmed by structural violence, and what data practices become possible when the goal is not institutional performance, but collective survival and dignity.

 

These commitments shape the book’s three-part structure in both methodical and revealing ways. Part I (“Data and Feminicide”) documents the practices, ethics, and labor of feminicide data activism, foregrounding activists’ own theories, methods, and struggles, situating them within feminist, decolonial, and data-activist traditions. Part II (“The Process of Restorative/Transformative Data Science”) theorizes these practices as a distinct epistemological and political approach to data science, what she calls restorative/transformative data science, by tracing the situated workflows through which data is resolved, researched, recorded, and sometimes refused. Part III (“Action–Reflection”) turns toward application and pedagogy, providing a participatory design case study and a practical toolkit for those seeking to undertake similar work in different domains of structural inequality. Across these sections, the book weaves together ethnographic interviews, participatory design research, theoretical synthesis, and reflexive critique without flattening activism into description or inflating theory into abstraction, keeping both tethered to the lived risks, negotiations, and constraints that activists navigate.

 

Reframing data science through the concept of restorative/transformative data science, in my view, serves as the book’s central theoretical contribution. Restoration refers to a reorientation of data away from extraction and toward healing, memory, dignity, and rights for communities marked by structural violence, while transformation refers to efforts to shift the structural conditions that produce that harm in the first place. It is not reformist ethics, but a refusal of the frameworks through which technological “solutions” are usually imagined. This dual orientation is what distinguishes the book from reformist approaches to “ethical AI” or “responsible data science,” which often seek to mitigate harm while leaving intact the corporate, state, and market structures that govern how data are collected, owned, analyzed, and used, including platform capitalism, bureaucratic governance, and data-driven policy regimes. The book’s wager crystallizes here: Data work is not neutral but constitutively political, a site of struggle over which lives are rendered legible, grievable, and actionable.

 

What I find particularly persuasive here is the book’s sustained refusal of technosolutionism. Throughout, D’Ignazio rejects the assumption that more data, better algorithms, or improved models can “solve” social inequality. Instead, she treats data science as a tactic: a particular mode of knowledge production deployed within already unequal and unjust information ecosystems. In contrast to Silicon Valley imaginaries of technological salvation, D’Ignazio advances a deliberately modest vision of data practice, one that is relational and necessarily partial, yet collectively powerful when embedded within broader networks of political action.

 

More concretely, chapter 7 presents a vivid illustration of this ethos through its account of a participatory design process with feminicide data activists. D’Ignazio opens the chapter with a fictional and deliberately absurd news blurb announcing that an “MIT professor has solved feminicide” (p. 217). This invented vignette functions as a critical foil, condensing the logics of technological heroism, academic saviorism, and extractive expertise that often shape institutional approaches to social injustice. The chapter then moves in the opposite direction, tracing instead a slow, relational process of co-design with activists grounded in trust-building, ethical negotiation, rather than solutionism. It is from this alternative model that the two tools emerge.

 

The first is a browser-based highlighter that allows activists to mark, annotate, and collect news reports of feminicide cases as they encounter them online, supporting careful, human-centered documentation rather than automated scraping or algorithmic classification. The second is an email alert system that circulates newly identified cases and updates among activists across different regions, helping them coordinate, verify information, and sustain a distributed network of care and accountability. These tools are intentionally limited in their technical ambition. They do not promise prediction, scale, or automation, but instead support practices of attentiveness, verification, relational labor, and mutual support. Read this way, the tools function as concrete experiments in how data infrastructures can be built with social movements rather than imposed on them. They model a form of data science oriented toward collaboration instead of extraction, toward care rather than control, and toward sustaining political relationships rather than optimizing technical systems. In doing so, they make visible the ethical and political negotiations that are usually erased from accounts of technological innovation, and they offer a grounded example of restorative/transformative data science in practice.

 

The concluding chapter steps back from the project of counting to reflect on what role data can—and cannot—play in struggles against structural violence. Here, D’Ignazio refuses both the fantasy of data as a neutral mirror of reality and a technical solution. Instead, she insists that data science is a modest and collective practice that participates alongside legal advocacy, community organizing, and care work in a broader ecology of struggle. Drawing on activists’ reflections, she emphasizes that data work is at once minimal and indispensable: insufficient on its own, yet capable of supporting, connecting, amplifying, and sustaining collective action.

 

At the same time, the book leaves open a set of tensions that feel both necessary and generative. Questions of scale, institutional support, and sustainability remain unresolved: How might restorative data infrastructures endure beyond academic funding cycles? How might such relational, care-based models operate at larger scales without reproducing hierarchy? The question of refusal sharpens these tensions further. How does refusal interact with demands for legal legibility and policy recognition? Refusal here does not mean a rejection of recognition as such, but a refusal of legibility on institutional terms, a refusal of extraction, classification, and visibility when these function as techniques of control rather than care. In this sense, refusal can operate as a form of protection against appropriation, surveillance, and epistemic capture, even as it sits uneasily with legal and policy regimes that hinge on standardization, documentation, and quantification. The tension remains unresolved, and productively so, because it exposes a limit internal to the project itself: The impossibility of fully reconciling care, protection, and institutional legibility within data practices, however feminist their orientation. These questions are not shortcomings so much as openings for further work.

 

While a book titled Counting Feminicide might be expected to focus on death, activists repeatedly remind D’Ignazio that their work is about defending life. The goal is not better counting but the eradication of violence and the creation of conditions under which women and gender-diverse people can live free from harm. In this sense, the book functions as a love letter, and perhaps even a form of political witnessing, for the networks of people doing the slow, difficult, and often invisible labor of care-based data activism. This reframing moves the book beyond necropolitical analytics toward an ethics of vitality, futurity, and collective survival, resonating strongly with feminist theories of care and interdependence.

 

 

Reference

 

D’Ignazio, C., & Klein, L. (2020). Data feminism. Cambridge, MA: MIT Press.

 

Copyright © 2026 (Muge Yuce, [email protected]).

Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd).

https://doi.org/10.65476/57fw4b30