Data Feminism
A Manifesto
Book •
Data Feminism, co-authored by Catherine D’Ignazio and Lauren F. Klein, examines the intersection of data science, algorithms, and feminist theory.
It challenges traditional data analysis methods, highlighting how biases and power structures influence data collection, analysis, and interpretation.
The book advocates for a more inclusive and equitable approach to data science, emphasizing the importance of considering diverse perspectives and lived experiences.
It provides practical strategies for identifying and mitigating bias, promoting critical thinking about data, and using data to advance social justice.
Data Feminism encourages a more ethical and responsible use of data, ultimately aiming to create a more just and equitable world.
It challenges traditional data analysis methods, highlighting how biases and power structures influence data collection, analysis, and interpretation.
The book advocates for a more inclusive and equitable approach to data science, emphasizing the importance of considering diverse perspectives and lived experiences.
It provides practical strategies for identifying and mitigating bias, promoting critical thinking about data, and using data to advance social justice.
Data Feminism encourages a more ethical and responsible use of data, ultimately aiming to create a more just and equitable world.
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Sean M. Carroll
156 | Catherine D’Ignazio on Data, Objectivity, and Bias
