This is one of the two main lessons of the module. We aim to give an overview of important concepts in EDI for data science and give many examples from the real world to create awareness about forms of oppression in DS/society and how they interact, how to detect and mitigate them.
This will contain interactive activities and be heavily informed by the material in Data Feminism. It will also be the basis for interrogating EDI issues in the hands-on session and the rest of the course.
Outline of this section
Forms of bias and oppression in data science and society, matrix of oppression, interactions
Common pitfalls, myths, examples (e.g. data do not lie, data scientist as unicorn, etc)
Forms of privilege (especially for DSs) and examples, e.g. related to race, gender, nationality
How to detect and challenge power and privilege
How DS affects communities and society and how to do participatory data science.
Some examples we can use
DS projects with community participation (several example in DF)
Examples of data collection (or non-collection) that reveal bias and ignorance of privilege.
Example of project with open, community-driven data collection vs. better-quality private data from DF.
Module 1.3: Intro to EDI for data science
Description
This is one of the two main lessons of the module. We aim to give an overview of important concepts in EDI for data science and give many examples from the real world to create awareness about forms of oppression in DS/society and how they interact, how to detect and mitigate them.
This will contain interactive activities and be heavily informed by the material in Data Feminism. It will also be the basis for interrogating EDI issues in the hands-on session and the rest of the course.
Outline of this section
Some examples we can use
Duration
60 minutes
Estimate time for developing/writing the section
20 hours