Prerequisites¶
| Prerequisite | Importance | Skill Level | Notes |
|---|---|---|---|
| Data Governance | Helpful | Beginner | Best practices on how data is collected and managed |
| Research Data Management | Helpful | Beginner | Covers how research data can be stored, described and reused. |
| Managing Sensitive Data Projects | Helpful | Beginner | Tips on managing sensitive data. |
Summary¶
💡 This chapter was written based on the book, Data Feminism by Catherine D’Ignazio and Lauren F. Klein.
Data feminism is an approach to understanding and practising data science that’s informed by the principles of feminist theory. It critiques traditional data practices that often overlook gender biases and other forms of inequality and advocates for a more inclusive, equitable, and reflective practice in data science
Here are some key aspects discussed within the chapter:
- Examine Power Structures: Data Feminism urges a critical examination of power dynamics in data practices. It questions who is represented in data, who benefits from data practices and whose interests are served.
- Challenge the Status Quo: This challenges existing norms in data science, such as the male/female binary and questions other hierarchical classification systems. Thus dismantling stereotypes to promote a more inclusive approach to data.
- Inclusivity and Representation: Data Feminism emphasises the need for diverse representation in data and in data teams. When data science is dominated by a homogeneous group, especially those from privileged backgrounds, it risks perpetuating biases and inequalities.
- Ethical Considerations and Consent: Ethical concerns such as consent and privacy are at the core of data feminism. This includes concerns about how data is collected and used and the potential for harm especially to marginalized communities.
- Visibility of Labour: This seeks to recognize and make the often unseen labour in data science visible; especially the contributions of women and people from marginalized groups.
- Interdisciplinary Approaches: Data feminism advocated for interdisciplinary work, drawing insights and methods from various fields outside of traditional data science to enrich understanding and foster more impactful outcomes.
- Action and Advocacy: Beyond analysis, data feminism promotes the need to advocate for change, challenge oppressive structures and promote social justice.
- Continual Learning and Adaptation: Data feminism acknowledges that anti-oppression work is never finished. Continuous learning is essential, and we must recognise society and technology’s dynamic nature.
In summary, data feminism is not just a theoretical framework but a practice that seeks to reshape how data is collected, analysed and used, focusing on equity, justice and dismantling traditional power structures.
Motivation and Background¶
Data feminism is rooted in the rich history of feminist activism and critical theory; it challenges the status quo in data practices by advocating for inclusivity and intersectionality.
Despite advancements, data science still reflects societal biases due to limited data collection and analysis perspectives.
The ethics of data collection, use and interpretation are increasingly under scrutiny. Issues around consent, privacy and the potential harm of data misuse are central to the data feminism discussion.
The impact of biased data practices is global, affecting policies, technology development and social attitudes. These global challenges must be addressed, and data feminism seeks to advocate more inclusive and representative data practices.