Data feminism begins with the process of data collection itself.
In a world where data yields power, it also becomes an essential tool for structural change.
In addition, biases in its analysis and presentation hinder social change or promote oppression.
Joni Seager, a feminist geographer, has rightly asserted—“what gets counted, counts”.
Collected data often becomes the basis for policymaking and resource allocation.
Therefore, it is essential to look at the collected data from every angle and ensure that it is all-inclusive.
Feminism understands the need for inclusivity better than most.
Feminism understands the need for inclusivity better than most.
When we look at feminism in its most advanced form, i.e. intersectional feminism, it is not only just a belief but an action towards the equality of the genders.
A term coined by Kimberlé Crenshaw, intersectional feminism is the idea that identities within and across social groups overlap and interplay, revealing that discrimination occurs at varying levels.
So, a person can experience multiple degrees of discrimination on the one hand and privileges on the other.
As such, when applied to data science, the feminist approach now seeks to challenge all systems of unequal power.
As such, when applied to data science, the feminist approach now seeks to challenge all systems of unequal power.