Catherine D’Ignazio and Lauren Klein’s paper\u00a0Feminist Data Visualization<\/em> provides insight on how those in the computational studies can better represent their data with equity.\u00a0 This paper brought together concepts, resulting in a guide for their term Feminist Data Visualization [D’Ignazio Pg 1].\u00a0 One idea that stood out to me was their point on rethinking binaries. In a space where booleans are in common use (and for good reasons), it is often easy to look at the inputs \/ outputs of an algorithm and determine that they are either correct or incorrect. D’Ignazio and Klien state that a powerful way to make your data more feminist is by doing data collection and classification while accounting for fluid categories [Pg 2]. This allows one to cover a wider breadth of data and have accurate representation, one specific example being gender.\u00a0 Some might say that this process of collection may lead to the creation of messy data. But it is important to note that computational tools are there to further sort and provide insight into that mess, allowing one to find new insights on the problem being investigated.<\/p>\n","protected":false},"excerpt":{"rendered":"

Catherine D’Ignazio and Lauren Klein’s paper Feminist Data Visualization provides insight on how those in the computational studies can better represent their data with equity.  This paper brought together concepts, resulting in a guide for their term Feminist Data Visualization [D’Ignazio Pg 1].  One idea that stood out to me was their point on rethinking binaries. … <\/p>\n

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