We're relying on data to describe the world. Let's do it well. This workshop explores some ways to bring strong ethics--as well as a genuine curiosity and optimism--to the data we use in our work.

1. Unflattening Data
Resisting Simple Solutions to Complex Problems

2. eROI
@eROI
2

3. I am not a data scientist.
But I use data in my work. I contribute data
about myself to the work of others. And I live,
like most of us here, in a world that is
increasingly mediated by the collection and
interpretation of data sets.
3

4. Metropolitan Museum of Art
Data is a descriptive system.
4

5. Edward Tufte
It tells us what we value. And how we value it.
5

6. The world always exceeds our efforts at description.
MTA, London Underground, TriMet6

7. All descriptions have a point of view.
Data is no exception.
Mimi Onuoha, Library of Missing Data Sets7

8. Collecting Data
“A museum is an exclusive space that brings some things in
and puts some things out. What’s interesting is we don’t
extend [this understanding] to how we define history, to how
we define data, or data sets.” —Sydette Harry
8 Art + Feminism: Careful with Each Other, Dangerous Together

9. Creating Algorithms
“Algorithms are opinions embedded in code.” —Cathy O’Neil
9 Ted2017: The Era of Blind Faith in Data Must End

10. What are the stakes here?
“Algorithms are abstracted from the humans and needs that
created them and there are always humans and needs behind
the algorithms we encounter every day.” —Mimi Onuoha
10 On Algorithmic Bias

11. So, how do we use this tool for good?
How have we ever improved any system we use?
We have to think carefully and listen well.
We have to bring our ethics and our creativity.
And we have to practice.
11

12. Dorothea Lange, Library of Congress12
“...any photograph
seems to have a more
innocent, and
therefore more
accurate relation to
reality.”
-Susan Sontag, On Photography

13. Who was Shirley?
The Eclectic Photography Resource13

14. A brief manifesto.
Data is a collective resource: we make it collectively
and we should collectively engage in creating its
meaning.
“Any human power can be resisted and changed by
humans.”—Ursula K. Le Guin
14 Speech at the National Book Awards

15. 15
Feeling a little
overwhelmed?
Sometimes it
helps to slow
down the inputs.
Modern Times

16. Dear Data, by Giorgia Lupi and Stefanie Posavec
16 Dear Data

17. 17

18. I think there’s a map in Dear Data.
I’m going to draw it in three stops. The routes are all questions.
William Sullivan18

19. Transparency
How would data look if:
We declared our point of view?
We could claim authorship and fully cite our sources?
We could know who is choosing what to look at and for how long?
We could know how they defined what they’re measuring?
We could know how they understand success?
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20. Relationality
How would we use data if:
We considered data science a reciprocal system?
We stopped Data Mining and started Data Exchange?
We worked to be in open conversation with those we’re observing?
We were always also observing ourselves and conveying those
observations?
Data collection and analysis were a form of community building?
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21. Inefficiency
How would data change if:
We cultivated patience with and even a wish for the untidy and
unresolved?
They are the friction that allows us to hold onto the world
as it is. They are also where we have something to learn.
There are no failed experiments.
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22. 22
Practice: The Map and the Territory

23. 23
Step One: Look
That’s it. Just look for a bit. Look at the image, and maybe,
a little bit, look at yourself looking at the image.

24. 24 Leonara Carrington

25. 25
Step Two: Plan
What are you interested in? Curious about?
What would you like to describe?
Can you use data to ask questions about what you see?
Can you share data that invites questions?
How can you practice transparency, relationality and inefficiency?
Make some notes, experiment a bit, then set your system.
(Remember: no failed experiments)

26. 26 Leonara Carrington

27. 27
Step Three: Record
Use your system to make a record of your observations.
Put a key on the back.

28. 28 Leonara Carrington

29. 29
How did it go?
Write down your impressions.
What did you notice?
Did you learn something about the image?
What was difficult? Frustrating?
Did you get surprised?
Did you think your system would be easy or difficult? Did it turn
out that way?

30. 30
We’re gonna try that one more time.

31. 31 David Hockney

32. 32
Talk Amongst Yourselves!
Common themes?
Successes or frustrations?
How about that three station map? How is that showing up (or
not)?

33. 33
Tell me everything.
(And here is a Selected Bibliography.)

34. 34
Thank you!
New York Times