Data Feminism
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- What do you not like about data science?
- Surveys:
- Corporations harvesting data to inform/support their bottom line.
- Survey questions are limited (e.g. linguistic limitations, yes/no answers only), that work to bolster the status quo, rather than validate the desire for change.
- Data is only gathered about populations that “matter” to those in positions of power (e.g. funders)
- Most data analysis is inherently extractive.
- Data security
- Data collection that doesn’t take into account lived experience (e.g. boxes for African and Latino, but not Afro-Latino)
- History of disciplines rooted in data and analysis like sociology and anthropology were/are rooted in white supremacy, and it’s important to be aware of those values persisting today.
- Quantitative data often prioritized over qualitative
- Surveys:
- “Data Feminism” the book, is based on teachings of the black feminist movement.
- 7 principals of data feminism:
- Examine power. Data feminism begins by analysing how power operates in the world.
- Challenge power. Data feminism commits to challenging unequal power structures and working toward justice.
- Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.
- Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
- Embrace pluralism. Data feminism insists that the most complete knowledge comes from synthesising multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
- Consider context. Data feminism asserts that data is not neutral or objective. It is the product of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
- Make labour visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognised and valued.
- 7 principals of data feminism: