---
tags: bookdash, 2023, may, chapter proposal, data feminism
---

`er-datafeminism`
# Data Feminism

*This chapter is based on reading notes from Catherine D'Ignazio and Lauren F. Klein's [Data Feminism](https://data-feminism.mitpress.mit.edu/) (2020).*

Data feminism calls attention to and questions power dynamics at work in data science pipelines and processes.

`er-datafeminism-prerequisites`

## Prerequisites

It may be useful for readers to consult the chapter on Self Reflection, specifially the subchapter on Positionnality.

| Prerequisite | Importance | Skill Level | Notes |
| -------------|----------|------|----|
| {ref}`Self reflection <er-selfreflection>` | Helpful| Beginner| ? |

It may also be useful for readers who are unfamiliar with feminist theory to consult some external resources such as Donna Haraway's definition of [intersetional feminism](https://disorient.co/feminist-standpoint-and-intersectionality/), or the same author's definition of [situated knowledge](http://www.jstor.org/stable/3178066).

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"Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective" Author(s): Donna Haraway Source: Feminist Studies, Vol. 14, No. 3 (Autumn, 1988), pp. 575-599 Published by: Feminist Studies, Inc. Stable URL: http://www.jstor.org/stable/3178066
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`er-datafeminism-summary`
## Summary

These are the sub-topics that this chapter will talk about:

Why Data Science Needs Feminism
Examine Power
Challenge Power
Elevate Emotion and Embodiment

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`er-datafeminism-motivation`
## Motivation and Background

This chapter may interest people who want to ensure data science projects respond to ethical criteria such as inclusion, equity and diversity.

The Influences section of the Self Reflection chapter directly cites Data Feminism as a resource:`{ref}Resources<er-selfreflection-resources>`.

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`er-datafeminism-whyfeminism`
# Why Does Data Science Need Feminism?

Data feminism explores current practices within data science and asks us to recognise and act on the role that power plays throughout research processes, from data collection and storage to communication and visualisation. This power is itself reflective of much wider social injustices.

Feminism provides "who" questions that may help to identify places where injustices are structurally embedded in the way data science, and science at large, are practiced.

Here are a few examples of questions to answer to make implicit power structures more apparent in data science projects :

- Data science by whom?

Big data collection is expensive, making it easier for wealthy institutions and or countries to engage in it.

- Data science for whom?

Asking this question can help explicitly engage in data science that serves women and other marginalized populations? (ex: Irth, a social entrepreneurship company for reproductive justice in the US)

- Data science with whose interests and goals?

This question reminds us to be watchful as data science is often incentivized by science, surveillance, selling.

Correlary questions:
- Who makes maps and who gets mapped?
- Who is it, exactly, that needs to be shown the harms of such differentials of power?
- On whom is the burden of proof placed?

## What does data feminism look like ?
- project conceptual framework based on equity and co-liberation
- project team made up of diverse set of people from multiple disciplinary backgrounds and diverse cultural competencies
- project takes oppression and inequality as grounding assumptions for creating computational products and systems
- project co-design and co-create with communities it seeks to support -> do this in a way that initially puts on the table the project team's intention to give something back to the community whose knowledge they will be drawing upon for their research (ask for a budget for this!)