This document provides an overview of applying an intersectionality lens in data science. It begins with defining intersectionality and explaining why it is important in data science. The rest of the document uses examples to illustrate how to apply an intersectionality perspective throughout the data science process, from problem definition and data collection to analysis, delivery and evaluation of results. It emphasizes avoiding assumptions, collecting data equitably, analyzing with care and presenting findings objectively. The document concludes by providing action steps for applying lessons around intersectionality to work.

1. Applying an
Intersectionality
Lens in Data
Science
1

2. Hello!
We are
Rachel Whaley, Sr. Database Manager, USC
Daphne Cheung, Data Scientist, Disney
2

3. Objectives
3
▫ Understand what intersectionality is
and why it matters in data science
▫ Provide tools for applications of an
intersectionality perspective in your
work

4. Agenda
4
01. What is Intersectionality?
02. Applying an Intersectionality Lens
03. Meet Jessica
04. Intersectionality in the Data Science Process
05. Action Items

5. 1.
What is
intersectionality?
A quick introduction
5

6. Intersectionality
6
female
Latinx
college
graduate

7. 2.
Applying an
Intersectionality Lens
7

8. “Intersectionality operates as both the
observance and analysis of power
imbalances, and the tool by which those
power imbalances could be eliminated
altogether.”
8
– Jane Coastan, Vox

9. How to Apply this Lens
9
Equitable Results
Define at the
beginning, based on
your project context
and domain
Unpacking
Assumptions
Identifying and
addressing power
imbalances
Awareness
Self-awareness,
understanding of
various groups,
and addressing
unconscious bias
Data Science
Project
Lifecycle

10. 3.
Meet Jessica!
10

11. Jessica is a data
scientist
working in her first role
in the human resources
department of a Fortune
500 technology
company.
111111

12. 4.
Intersectionality in the
Data Science Process
12

13. Problem
Definition
DataCollection3.Analysis and
Training
4.Evaluation
andDelivery1. Problem
Definition
2.Data
Collection
The Data
Science Project
Lifecycle
13

14. Problem Definition
Understand and predict that female
employees are more likely to churn than
their male counterparts at the company.
What’s wrong with Jessica’s problem
statement?
14
Problem
Definition
DataCollection
3.Analysis and
Training
4.Evaluation
andDelivery
1. Problem
Definition
2.Data
Collection

15. Data Collection
15
Jessica will collect churn data based on
the following factors:
▫ Gender
▫ Age
▫ Race
What’s wrong with Jessica’s data
collection approach?
Problem
Definition
DataCollection
3.Analysis and
Training
4.Evaluation
andDelivery
1. Problem
Definition
2.Data
Collection

16. Analysis and Algorithm Training
16
Jessica trains her prediction model,
which tells her that women are more
likely to churn than men. She’s ready to
present her findings!
Is Jessica actually ready to present her
findings?
Problem
Definition
DataCollection
3.Analysis and
Training
4.Evaluation
andDelivery
1. Problem
Definition
2.Data
Collection

17. 17
Delivery
Jessica shares the results of her
analysis.
How can Jessica prevent misinterpretation
of her findings?
Problem
Definition
DataCollection
3.Analysis and
Training
4.Evaluation
andDelivery
1. Problem
Definition
2.Data
Collection

18. 5.
Action Steps
How to apply what you’ve learned to your work
Get the one-pager: latech4good.org/intersectionality 18

19. 19
Deepen Awareness
Self-awareness and
unconscious bias training
for data teams.
19latech4good.org/intersectionality 19

20. Unpack Assumptions
Document and challenge
assumptions that arise and
consult peers representative
of your audience.
20latech4good.org/intersectionality 20

21. Design Effectively
Approach objectively
and eliminate
confirmation bias.
21latech4good.org/intersectionality 21

22. Collect Equitably
Disaggregate data for
representation of
intersectional groups.
22latech4good.org/intersectionality 22

23. Analyze with Care
Ask appropriate
questions when
exploring the data.
23latech4good.org/intersectionality 23

24. Present Objectively
Deliver data objectively
and ensure accurate
representation of
identities.
24latech4good.org/intersectionality 24

25. Evaluate Results
Define what equitable
results will look like.
Revisit design process if
not achieved.
25latech4good.org/intersectionality 25

26. 26
Questions?
Daphne Cheung
You can find me at:
▫ daphnecheung2@gmail.com
▫ linkedin.com/in/daphnecheung1
Rachel Whaley
You can find me at:
▫ @rachelrwhaley
▫ linkedin.com/in/rachelrwhaley
Get the one-pager: latech4good.org/intersectionality

27. And tables to compare data
A B C
Yellow 10 20 7
Blue 30 15 10
Orange 5 24 16
27

28. Maps
our office
28
Find more maps at slidescarnival.com/extra-free-resources-icons-and-maps

29. 89,526,124
Whoa! That’s a big number, aren’t you proud?
29

30. 89,526,124$
That’s a lot of money
100%
Total success!
185,244 users
And a lot of users
30

31. Our process is easy
31
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32. You can insert graphs from Google Sheets
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33. Mobile project
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or software projects using these
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34. Place your screenshot here
34
Tablet project
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or software projects using these
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35. Place your screenshot here
35
Desktop project
Show and explain
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software projects
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templates.

36. Intersection
ality
Bring the attention of your
audience over a key concept
using icons or illustrations
36

37. Credits
Special thanks to all the people who made and
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▫ Presentation template by SlidesCarnival
▫ Photographs by Unsplash
37

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40. Diagrams and infographics
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41. ✋👆👉👍👤👦👧👨👩👪💃🏃
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