eXplainable Artificial Intelligence is supposed to be easy to understand for humans. This means that it needs to be properly design. In this presentation Przemek shows why XAI is an important topic and Hanna shows how to design visual presentation for predictive models. Presented by Przemyslaw Biecek and Hanna Piotrowska at satRdays Gdansk 2019

2. Why do we need
XAI?

4. https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/

5. https://www.massdevice.com/report-ibm-watson-delivered-unsafe-and-inaccurate-
cancer-recommendations/

7. https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

10. !10

12. Meet DALEX

19. How to explain visually?

20. 1/ Why bother?

21. Data visualization is a visual representation
of abstract data to amplify cognition
stuart card

22. Data visualization is a visual representation
of abstract data to amplify cognition
stuart card

23. 2/ The goals

24. 1/ readability
2/ accessibility
3/ consistency

25. 1/ readability
2/ accessibility
3/ consistency
efficiency

26. 3/ The changes

27. svm
rf
lm
250 500 750 1000 1250
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Drop−out loss
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●
●
0
250
500
750
1000 2000 3000 4000
life_length
residuals
label
●
●
lm
rf
Residuals vs life_lengthsurface
40 80 120
3000
3300
3600
ʻ_x_ʻ
ʻ_yhat_ʻ
hours
40 50 60 70 80
0.00
0.25
0.50
0.75
ʻ_x_ʻ
ʻ_yhat_ʻ
ʻ_label_ʻ
fired
ok
promoted

28. 3000
3200
3400
surface
prediction
3600
3800
Surface
6019 100 148
Random Forest
Random Forest
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
2
3
4
4
5
6
6
7feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
Drop-out loss
Variable importance
GBM
baseline
ditrict
surface
floor
construction.year
no. rooms
full model
250 500 750 1000 1250
Distribution of residuals
residualspercentage
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100%
5000 1000 1500 2000 2500
rf gm m3
Distribution of residuals
lm
mod1
lr
Model Ranking
Model 01
Model 02
Model 03
Model 04
Model 05
Model 06
invSC
invSC
invREC
invROCC
invMAE
invMSE
invREC
invROCC
invMAE
invMSE
invMAE
0.81 0.68 1 0.99 0.7 0.47 0.59 1 0.98 1
0.71 1 0.98 1 1 0.5 1 0.82 0.81 0.65
0.990.72 0.63 1 0.7 0.7 0.91 0.7 0.7 0.7 0.85
0.780.82 0.8 0.5 0.5 0.62 0.69 1 0.65 0.69 0.65
10.82 0.63 0.8 1 0.8 0.87 0.7 1 0.7 1
0.81 0.63 1 0.7 0.8 0.6 1 1 0.69 0.98
1000489 2000 3000 4043
life_lenght
residuals
Residuals vs life lenght
0
250
500
750
GMB Random Forest LM
40
80
100
60
1920 1940 1960 1980 2000
construction.year
surface
120
Surface vs construction year
5000
4000
2000
3000
1000
Prediction
& residual
value
residual

29. feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
BreakDown

30. FactorMerger

31. FactorMerger

32. FactorMerger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
3
3
4
4
4
4
4
4
Random Forest
LM
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
2
3
4
4
5
6
6
7

33. Ceteris Paribus Profiles

34. Ceteris Paribus Profiles
3000
2900
3200
3400
surface
prediction
3600
3800
District impact on surface
6020 100 144
Bemowo Mokotow Ochota Srodmiescie Ursus

35. Ceteris Paribus Profiles
3000
2900
3200
3400
surface
prediction
3600
3800
District impact on surface
6020 100 144
Bemowo Mokotow Ochota Srodmiescie Ursus

36. The colors
#8bdcbe
Main colors
#f05a71
#371ea3
#46bac2
#ae2c87
#ffa58c
#4378bf
#160e3b
Additional colors
#f0f0f4
#ceced9
model2lrmod1lm rf gm m3

37. The colors
#8bdcbe
Main colors
#f05a71
#371ea3
#46bac2
#ae2c87
#ffa58c
#4378bf
#160e3b
Additional colors
#f0f0f4
#ceced9
model2lrmod1lm rf gm m3

38. The colors
#8bdcbe
Main colors
#f05a71
#371ea3
#46bac2
#ae2c87
#ffa58c
#4378bf
#160e3b
Additional colors
#f0f0f4
#ceced9
model2lrmod1lm rf gm m3

39. The colors
https://www.economist.com/finance-and-economics/2016/09/03/more-spend-less-thrift
Deuteranopia simulationOriginal graphic

40. The colors
#8bdcbe
#8bdcbe
position: 25%
#c7f5bf
position: 0%
#f05a71 #371ea3 #46bac2
#46bac2
position: 50%
#ae2c87 #ffa58c #4378bf
#4378bf
position: 75%
Main colors
Gradients
#160e3b #f0f0f4#ceced9
alpha 50%
Additional colors
#c7f5bf #b3eebe #9fe5bd #8bdcbe #77d1be #61c5c0 #46bac2 #45a6c4 #4590c4 #4378bf #415fb9 #3d42af #371ea3
#371ea3
position: 100%
#77d1be
position: 25%
#9fe5bd
position: 0%
#46bac2
position: 50%
#4590c4
position: 75%
#9fe5bd #92debd #84d8be #77d1be #67c9bf #56c2c1 #46bac2 #46acc3 #459ec3 #4590c4 #4480c0 #426fbd #371ea3
#371ea3
position: 100%

41. The colors

42. label
label
label
x axis title
yaxistitle
label
label
Chart Title
labellabel label label label
25
30
8
15
20
25
30
8
15
20
81520 20
The proportions

43. The proportions
8 8
1000489 2000 3000 4043
life_lenght
residuals
Residuals vs life lenght
0
250
500
750
otherRandom Forest
Chart panel:
aspect ratio 3:2
Chart panel center aligned horizontally
25
30
8
minimal margin
fixed margin
8
15
20
81520 20
Title:
Fira Sans SemiBold 18pt
Legend:
Fira Sans Regular 11pt
Axis Title:
Fira Sans Regular 13pt
Axis Title:
Fira Sans Regular 13pt
Axis Labels:
Fira Sans Regular 11pt
Axis Labels:
Fira Sans Regular 11pt
Line:
2px
#371ea3
Dots:
2px
#46bac2Inactive:
#ceced9
50%

44. The proportions
minimal margin
fixed margin
Axis Labels:
Fira Sans Regular 11pt
Axis Title:
Fira Sans Regular 13pt
8
10
15
20
820 20
Title:
Fira Sans SemiBold 18pt
Small multiple title:
Fira Sans SemiBold 13pt
Axis Labels:
Fira Sans Regular 11pt
Prediction Label:
Fira Sans SemiBold 11pt
25
30
8
10
Bars: 12 px
Space: 6 px
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
Values:
Fira Sans Regular
11pt
white background
Prediction value:
Fira Sans SemiBold
11pt, white background
Intercept line: 0.5px,
dash: 1px, space: 2px
Line: 0.5px

45. Variable importance
GBM
baseline
ditrict
surface
floor
construction.year
no. rooms
full model
Drop-out loss
250 500 750 1000 1250
Bars: 12 px
Space: 6 px
Bars: 12 px
Space: 6 px
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
Random Forest
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
2
3
4
4
5
6
6
7
Bars: 12 px
Space: 6 px

46. 30
8
10
Variable importance
GBM
baseline
ditrict
surface
floor
construction.year
no. rooms
full model
Drop-out loss
250 500 750 1000 1250
Bars: 12 px
Space: 6 px
30
8
10
Bars: 12 px
Space: 6 px
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
8
10
Random Forest
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
2
3
4
4
5
6
6
7
30
8
10
8
10
Bars: 12 px
Space: 6 px

47. minimal margin
fixed margin
25
30
8
10
8
10
15
20
Variable importance
GBM
baseline
ditrict
surface
floor
construction.year
no. rooms
full model
Drop-out loss
250 500 750 1000 1250
Bars: 12 px
Space: 6 px
20
25
30
8
10
Bars: 12 px
Space: 6 px
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
8
10
Random Forest
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
GROUP FREQUENCYNAME PRICE MEAN
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
NR
1
2
2
3
4
4
5
6
6
7
25
30
8
10
8
10
Bars: 12 px
Space: 6 px
20

48. 1/ final effect = needs + goals + constraints*
* plus a bit of fun and looking at other beautiful charts for inspiration

49. 1/ final effect = needs + goals + constraints*
2/ solutions <- “why” & “what for” questions
* plus a bit of fun and looking at other beautiful charts for inspiration

50. 1/ final effect = needs + goals + constraints*
2/ solutions <- “why” & “what for” questions
3/ science + design = ∞
* plus a bit of fun and looking at other beautiful charts for inspiration

51. Meet DALEX 2.0

52. Predictive Models: Visual Exploration, Explanation and Debugging
Production
Development
Concept ValidateForge
Model debugging
Model development is an
iterative process. Each
iteration brings new insights.
Early phases:
Crisp modeling, general
understanding of the problem.
Medium phases:
Selective modeling, here we
select the best type of model.
Late phases:
Fine tuning of model
parameters or variable
engineering .
In each iteration model development starts with some concepts, ideas, then the model is trained and finally
model needs to be validated.
Predictions need to be
explained.
Here the instance level
explanation helps.
With time the model
performance may deteriorate,
thus it requires constant
monitoring, e.g. with the drifter
package.
Drop-out loss
Variable importance
GBM
baseline
ditrict
surface
floor
construction.year
no. rooms
full model
250 500 750 1000 1250
3000
3200
3400
surface
prediction
3600
3800
Surface
6019 100 148
Random Forest
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
Random Forest
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
LM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700 2800 2900
2046
2614.9
+358
+160
+78
-39.5
+12.4
2800
2425
-338
-112
+74
-53
+26
2378
2324.9
-239
+160
+68
+39.5
-12.4
feature influence
Variables attributions
GBM
intercept
district: Srodmiescie
surface: 22
no.rooms: 2
construction.year: 2005
floor: 1
prediction
2000 2100 2200 2300 2400 2500 2600 2700
2046
2614.9
+358
+160
+78
-39.5
+12.4
3000
3200
3400
surface
prediction
3600
3800
Surface
6019 100 148
Random Forest
3000
3200
3400
2 4 6
prediction
3600
3800
3000
3200
3400
3600
3800
Surface
1920 1940 1960 1980 2010
no.roomsfloor
surfaceconstruction.year
4020 80 120 144
2.50.8 1.15.0 7.5 11.2
Variable selection
Feature engineering
Random Forest
Factor Merger
Srodmiescie
Ochota
Mokotow
Zoliborz
Ursus
Bielany
Bemowo
Wola
Ursynow
Praga
0 2000 4000
group frequencyname price mean
5109.19
3954.83
3946.96
3918.55
3058.52
3045.79
3028.58
3011.69
3009.72
2991.48
nr
1
2
2
3
4
4
5
6
6
7
Prediction explanations What-If analysis Concept drift detection
explain

55. https://www.encyclopedia-titanica.org/
What are the odds of surviving?

58. What next?

60. https://kmichael08.github.io

61. https://github.com/MI2DataLab/modelDown

62. https://chudekm.shinyapps.io/model_explorer_example/

65. Thank you