2019 Women in Analytics Conference Jennifer Prendki womeninanalytics.com

1. Sustainable Machine
Learningwww.alectio.com
The Importance of Ethics in Data
Science
MAKING AI TEAMS WORK IN THE REAL WORLD
Jennifer Prendki, PhD
Founder & CEO, Alectio
WIA Conference, Columbus, OH
March 2019

2. Jennifer Prendki, PhD
Founder and CEO, Alectio
More about me:
• Currently Expert Network @ IIA
• Previously VP of Machine Learning @ Figure Eight,
Chief Data Scientist @ Atlassian
• Managed Applied Data Science Research in the Search team
@ Walmart Labs
• Have built & scaled ML functions in companies of all sizes

3. ALECTIO’S MISSION:
Sustainable Machine Learning
Helping Machine Learning teams build Machine
Learning models with less resources (starting with
less data)

4. AGENDA
• Data: The New Oil?
• Fatally Unprepared?
• Data At All Costs?
• Insane(ly Good) Machine Learning
• Responsible Data Science
ETHICS IN DATA SCIENCE AND MACHINE LEARNING

5. Data: The New Oil?
WHY WE DATA SCIENTISTS LOVE OUR DATA…

6. An Explosion of Data

7. An Explosion of Data
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
Digital Data
Growth
Exabytes

8. An Explosion of Data
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
Digital Data
Growth
1 Minute on the Internet…
Exabytes

9. Big Data and Machine Learning
Computer vision
“invented”

10. Big Data and Machine Learning
Computer vision
“invented”
ImageNet Classification Error (Top 5)
26.0
16.4
11.7
7.3
6.7
5.0
3.6 3.1
30.0
25.0
20.0
15.0
10.0
5.0
0.0
2011
(XRCE)
2012
(AlexNet)
2013 (ZF) 2014 (VGG) Huma
n
2015
(ResNet)
2016
(GoogleNet-
v4)
2014
(GoogleNet
)

11. Big Data and Machine Learning
• 14,000,000 labeled images
• 20,000 categories
Computer vision
“invented”
ImageNet Classification Error (Top 5)
16.4
11.7
7.3
6.7
5.0
3.6 3.1
30.0
25.0
20.0
15.0
10.0
5.0
0.0
2011
(XRCE)
2012
(AlexNet)
2013 (ZF) 2014 (VGG) 2014
(GoogleNet
)
Huma
n
2015
(ResNet)
2016
(GoogleNet-
v4)
26.0

12. “ Data is Tech’s New Drug. ”

13. “ Data is Tech’s New Drug. ”
“ Data is The New Plastic. ”

14. Fatally
Unprepared?
THE IMPACT OF THE BIG DATA ECONOMY ON SOCIETY

15. Socially fit?
100%
48% 85%
27%
94%
38%
46%
100%
82%
26%

16. Are you popular with A.I.?
AndreyPopov|iStock|GettyImages

17. A.I. watching your every move…
AndreyPopov|iStock|GettyImages

18. The Future Has Arrived Today
”Meet The Robinsons” | Disney®

19. The Future Has Arrived Today
”Meet The Robinsons” | Disney®

20. Progress… or Global Societal Abuse?
Disappearance of Privacy

21. Progress… or Global Societal Abuse?
Disappearance of Privacy
Abuses of the Data
Economy
$

22. Progress… or Global Societal Abuse?
Disappearance of Privacy Automation of Unfairness
Abuses of the Data
Economy
$

23. Progress… or Global Societal Abuse?
Disappearance of Privacy Automation of Unfairness
Abuses of the Data
Economy
Malevolent Applications
$

24. Data At All Costs?
THE IMPACT OF THE BIG DATA ECONOMY ON SOCIETY

25. Datafication:
a modern technological trend turning
many aspects of our lives into data which
is subsequently transferred into
information realized as a new form of
value.

26. Google
Trends
GDPR
Data Protection Officer
Privacy
Ethics

27. A Brief History of Data Privacy
Google
Street View
Behavior
targeting
is targeted
Facebook Apps
harvesting data
w/out consent
Voicemail
Hacking
Facebook &
Cambridge Analytica
GDPR
EU Treaty went
into effect
Creation of the
European Data
Protection Directive
Privacy in the
News
Proposal of
GDPR Released
Adoption by the
EU Parliament
GDPR valid

28. Data Labeling and the Gig Economy
The human side of A.I.

29. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities

30. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…

31. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…

32. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…

33. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…

34. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…

35. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…
 Slow human <> job matching
 Overall

36. Data Labeling and the Gig Economy
The human side of A.I.
o A dependency on human labor
 Good side: communities
o A challenge for the workers
 A tougher job than it might seem…
 Slow human <> job matching
 Overall
o Inconsistent qualify of work
 Error-prone tasks
 Subjective tasks

37. Insane(ly good)
Machine Learning?
THE DR. JEKYLL AND MR. HIDE OF THE TECH WORLD

38. The Dark Side of Machine Learning
June 2015:
Google labels a black woman as a
gorilla

39. Biases All Over the Place…

40. Biases All Over the Place…
DATA BIAS
o Labeling Bias
 Subjective Labeling Tasks
o Subgroup Validity
 Simpson’s Paradox
o Representation
 Inappropriate Sampling Strategy

41. Biases All Over the Place…
DATA BIAS ALGORITHMIC
BIAS
o Labeling Bias
 Subjective Labeling Tasks
o Subgroup Validity
 Simpson’s Paradox
o Representation
 Inappropriate Sampling Strategy
o Involuntary
 Statistical Stereotyping
o Voluntary
 Agenda-Based

42. “ Data is the Reflection of our
Society. ”

43. “ Data is the Reflection of our
Society. ”
… Machine Learning cannot fabricate Objectivity

44. License to Discriminate?

45. License to Discriminate?

46. License to Discriminate?

47. Explainability & Transparency

48. Responsible
Data Science
THE FUTURE OF DATA IS SPELLED E-T-H-I-C-S

49. A Scary World Ahead?

50. A Fairer AI Economy
o General Patterns > Granular Insights

51. A Fairer AI Economy
o General Patterns > Granular Insights
o Social Impact > Feasibility

52. A Fairer AI Economy
o General Patterns > Granular Insights
o Social Impact > Feasibility
o Human + Machine Collaboration > Competition

53. A Fairer AI Economy
o General Patterns > Granular Insights
o Social Impact > Feasibility
o Human + Machine Collaboration > Competition
o Ethics by Design > Legislation

54. Fairness vs. Biases
• With ML, biases are of the essence… and that’s a good thing!
• (Yes, you read that right!)

55. Fairness vs. Biases
• With ML, biases are of the essence… and that’s a good thing!
• (Yes, you read that right!)
• Fairness is not ingrained in Machine Learning
• Machines learn what we humans teach them
• (Yes, even in the case of Reinforcement Learning)

56. Fairness vs. Biases
• With ML, biases are of the essence… and that’s a good thing!
• (Yes, you read that right!)
• Fairness is not ingrained in Machine Learning
• Machines learn what we humans teach them
• (Yes, even in the case of Reinforcement Learning)
Unfairness ≠ Bias
ML is born of biases, but its societal purpose dies with unfairness

57. Responsible A.I.
o Ethical
o Inclusive (not exclusive to a privileged group)
o No harm to society (no weaponization)
o Centered on the well-being of Society

58. Be the Change you
want to see in the World
o Machine Learning will not become fair on its
own
 ML algorithms are by-products of human-generated
data
o Society and politicians are not ready
 Uneducated users
 No appropriate legislation in place
o The one true prevention of unethical use of
data is the Data Community
CongresshearingofMarkZuckerberginApril
2018