# data feminism
**Wikidata**: [Q111721257](https://www.wikidata.org/wiki/Q111721257)  
**Source**: https://4ort.xyz/entity/data-feminism

## Summary
Data feminism is a feminist approach to data science that critiques how data is collected, analyzed, and used, emphasizing the need for ethical and inclusive practices. It challenges traditional data science by highlighting biases, power structures, and marginalized voices, advocating for more equitable and representative data systems.

## Key Facts
- **Subclass of**: Feminism and data science
- **Aliases**: Feminismo de los datos
- **Named after**: Data Feminism
- **Sitelink count**: 1
- **Wikipedia languages**: Italian (it)
- **Parent field**: Data science

## FAQs
### Q: What is the goal of data feminism?
A: Data feminism aims to make data science more ethical and inclusive by addressing biases, power imbalances, and the exclusion of marginalized voices in data collection and analysis.

### Q: How does data feminism differ from traditional data science?
A: Unlike traditional data science, which often prioritizes efficiency and scalability, data feminism focuses on equity, representation, and the ethical implications of data use.

### Q: Who is associated with data feminism?
A: The term "Data Feminism" was coined by Catherine D’Ignazio and Lauren F. Klein in their 2020 book of the same name, which outlines the principles and practices of the movement.

## Why It Matters
Data feminism is significant because it challenges the dominant narratives in data science, which often reflect and reinforce existing power structures. By centering marginalized voices and ethical considerations, it seeks to create more just and inclusive data systems. This approach is crucial in an era where data influences policy, technology, and social outcomes, ensuring that these systems do not perpetuate harm. The movement also provides a framework for practitioners to engage critically with data, fostering a more equitable and representative data landscape.

## Notable For
- **Ethical critique**: Data feminism explicitly critiques the biases and exclusions in traditional data science.
- **Inclusivity focus**: It advocates for data systems that prioritize marginalized voices and perspectives.
- **Academic foundation**: The movement is grounded in the 2020 book *Data Feminism* by Catherine D’Ignazio and Lauren F. Klein.
- **Cross-disciplinary approach**: It combines feminism with data science, offering a unique lens for analyzing and addressing data-related issues.
- **Limited Wikipedia presence**: Currently, data feminism is only documented in Italian Wikipedia, indicating its emerging status in the broader discourse.

## Body
### Origins
Data feminism was formally introduced in 2020 with the publication of *Data Feminism* by Catherine D’Ignazio and Lauren F. Klein. The book outlines the principles and practices of the movement, emphasizing ethical considerations in data science.

### Key Principles
- **Equity**: Data systems should prioritize fairness and representation.
- **Inclusivity**: Marginalized voices must be centered in data collection and analysis.
- **Ethical critique**: The movement challenges traditional data science practices that perpetuate harm.

### Impact
- **Academic influence**: The book has been influential in shaping discussions around ethical data practices.
- **Cross-disciplinary engagement**: Data feminism has been adopted in fields beyond computer science, including social sciences and humanities.

### Current Status
- **Wikipedia presence**: Limited to Italian Wikipedia, reflecting its relatively new status in the broader discourse.
- **Growing recognition**: The movement continues to gain traction as data ethics becomes a more prominent concern.