Presentation on various modeling and visualization techniques to help address common types of bias in machine learning, taking steps towards more inclusive data science. Discusses: - Sampling - Role of user research - Visualization techniques - Narrative strategies

1. User Centered
Machine Learning
Steps Towards More Inclusive Data Science
Modeling Visualiza9on Tips

2. Modeling Visualiza9on Tips
Sample
Perspec9ve
How can we model
with inclusion and
respect?
Evalua9on
Background

3. Modeling Visualiza9on Tips
Sample
Perspec9ve
There have been some things to
consider…
Evalua9on
Background

4. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Inequality in access
to products
Visualiza9on Tips

5. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Certain types of
segmentaCons
Visualiza9on Tips

6. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Unexpected disclosure
of personal informaCon
Visualiza9on Tips

7. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
At risk populaCons
and quesConable informed
consent
Visualiza9on Tips

8. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Civil liberCes
Visualiza9on Tips

9. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Civil liberCes
Visualiza9on Tips

10. Modeling
Sample
Perspec9ve
Some things to think about…
Evalua9on
Background
Possibly accidental
discriminaCon
Visualiza9on Tips

11. Modeling
Sample
Perspec9ve
(could be beHer)
Evalua9on
Background
Visualiza9on Tips

12. Modeling
Sample
Perspec9ve
What causes this?
Evalua9on
Background
Visualiza9on Tips

13. Modeling
Sample
Perspec9ve
What causes this?
Evalua9on
Background
Bias of the corpus
Visualiza9on Tips

14. Modeling
Sample
Perspec9ve
What causes this?
Evalua9on
Background
Bias in the sampling
Visualiza9on Tips

15. Modeling
Sample
Perspec9ve
What causes this?
Evalua9on
Background
0
200
400
600
800
English German Kabyle French Catalan
Hours
Bias in the contribuCon
Visualiza9on Tips

16. Modeling
Sample
Perspec9ve
Take away 1: Measure and respond
to bias in samples.
Evalua9on
Background
Visualiza9on Tips

17. Modeling
Sample
Perspec9ve
Evalua9on
Background
Mathema9cal mi9ga9on
Visualiza9on Tips
Take away 1: Measure and respond
to bias in samples.

18. Modeling
Sample
Perspec9ve
Evalua9on
Background
Mathema9cal mi9ga9on
Resample or
Instance Weights
Visualiza9on Tips
Take away 1: Measure and respond
to bias in samples.

19. Modeling
Sample
Perspec9ve
Evalua9on
Background
Mathema9cal mi9ga9on Visualiza9on
Resample or
Instance Weights
Visualiza9on Tips
Take away 1: Measure and respond
to bias in samples.

20. Modeling
Sample
Perspec9ve
Evalua9on
Background
Visualiza9on Tips
Take away 1: Measure and respond
to bias in samples.
It’s not about fault but it is part of
the data scien9st’s job to check this.

21. Modeling
Sample
Perspec9ve
Evalua9on
Background
There’s something during modeling
too.
Visualiza9on Tips

22. Modeling
Sample
Perspec9ve
Evalua9on
Background
What is your perspecCve?
How are usage paHerns
understood for marginalized
communi9es?
Visualiza9on Tips

23. Modeling
Sample
Perspec9ve
Evalua9on
Background
What is your perspecCve?
What does it mean to train ML when
your social norms for the technology
do not match those of your users?
Visualiza9on Tips

24. Modeling
Sample
Perspec9ve
Evalua9on
Background
What is your perspecCve?
How do you value a community you
may not be part of?
… if you are in charge of technology that
“mediates” their experience?
Visualiza9on Tips

25. Modeling
Sample
Perspec9ve
Evalua9on
Background
What is your perspecCve?
Visualiza9on Tips
What’s the solu9on?

26. Modeling Visualiza9on Tips
Sample
Perspec9ve
Evalua9on
Background
Take away 2: Do user research…
qualitaCve if possible
and also encourage
diversity

27. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
Visualiza9on Tips

28. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
What is a house?
Visualiza9on Tips

29. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
What is a dwelling space?
Visualiza9on Tips

30. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
Did it give liR in posCng
updates?
Visualiza9on Tips

31. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
Did it give liR in posCng
updates => did it give liR
in general acCvity?
Visualiza9on Tips

32. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
Precision and recall
Visualiza9on Tips

33. Modeling
Sample
Perspec9ve
Evalua9on
Background
How do you evaluate your models?
Precision and recall => precision and recall per
Visualiza9on Tips
country
language
age

34. Modeling
Sample
Perspec9ve
Evalua9on
Background
Take away 3:
EvaluaCon metrics are
an oRen overlooked
place for bias.
Visualiza9on Tips

35. Modeling
Sample
Perspec9ve
Evalua9on
Background
Take away 3:
Evalua9on metrics are an oPen
overlooked place for bias. Write
them inclusively and evaluate in
different segments.
Visualiza9on Tips

36. Modeling Visualiza9on Tips
How can you build tools to help?
Dimensionality
Individual in Aggregate
Explainability

37. Modeling Visualiza9on Tips
Embrace dimensionality
Dimensionality
Individual in Aggregate
Explainability

38. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability
What is a dwelling space?

39. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability
(low fidelity wireframes)

40. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability
(not real data. not beau9ful.)

41. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability

42. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability

43. Modeling Visualiza9on Tips
HypotheCcal dashboard
Dimensionality
Individual in Aggregate
Explainability

44. Modeling Visualiza9on Tips
Take away 4: Create “layered”
visualizaCons to invite
progressively more
sophisCcated and
highly dimensional
readings
Dimensionality
Individual in Aggregate
Explainability

45. Modeling Visualiza9on Tips
How do we make this
explainable?
Dimensionality
Individual in Aggregate
Explainability

46. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Rephrasing metrics
93% recall
97% precision

47. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Rephrasing metrics
93% recall
97% precision
The model iden9fied 93% of the
dwelling spaces of those that
could be iden9fied
When the model said something
was a dwelling space, it was right
97% of the 9me.

48. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Rephrasing metrics

49. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Take away 5: Rephrase technical
terms into explanaCons

50. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Telling users
Are you in a posi9on where you
can tell the user about the use of
the algorithm?

51. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Telling users
93% recall
97% precision
This map is constructed from
satellite imagery looking for
dwelling places.
The model iden9fied 93% of the
dwelling spaces. When the model
said something was a dwelling
space…

52. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Take away 6: Be transparent with
users about the use of ML.
93% recall
97% precision
This map is constructed from
satellite imagery looking for
dwelling places.
The model iden9fied 93% of the
dwelling spaces. When the model
said something was a dwelling
space…

53. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
There’s nuance in user
engagement we don’t have 9me
for today.

54. Modeling TipsVisualiza9on
Dimensionality
Individual in Aggregate
Explainability
How can viz be
respec_ul of the
individual?

55. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
podcastanthropology.com

56. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Kim Reas

57. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability

58. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Take away 7: Provide access to the
individual when displaying in
aggregate.

59. Modeling Visualiza9on Tips
Wrap it up..

60. Modeling Visualiza9on Tips
Quick review!

61. Modeling Visualiza9on Tips
Mathema9cal mi9ga9on Visualiza9on
Resample or
Instance Weights
Take away 1: Measure and respond
to bias in samples.

62. Take away 2: Do user research…
qualitaCve if possible
Modeling Visualiza9on Tips

63. Modeling Visualiza9on Tips
Take away 3:
Evalua9on metrics are an oPen
overlooked place for bias. Write
them inclusively and evaluate in
different segments.

64. Modeling Visualiza9on Tips
Take away 4: Create “layered”
visualizaCons to invite
progressively more
sophisCcated and
highly dimensional
readings

65. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Take away 5: Rephrase technical
terms into explanaCons

66. Modeling Visualiza9on Tips
Dimensionality
Individual in Aggregate
Explainability
Take away 6: Be transparent with
users about the use of ML.
93% recall
97% precision
This map is constructed from
satellite imagery looking for
dwelling places.
The model iden9fied 93% of the
dwelling spaces. When the model
said something was a dwelling
space…

67. Modeling Visualiza9on Tips
Take away 7: Provide access to the
individual when displaying in
aggregate.

68. Thanks!
Sam PoZnger, Data Scien9st
Data Driven Empathy LLC
hHps://gleap.org

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