The document discusses the necessity of an ethical framework for data science, highlighting the significant impact data science has on various sectors and the importance of establishing credibility and responsibility within the profession. It emphasizes the concept of moral hazard, where decisions affect individuals indirectly, and underlines the various components that should be included in a potential data ethics code. Additionally, it notes that ethical practices in data handling can mitigate risks and present a competitive advantage in the market.

1. DATA
Tim Rich
Director of Data Science
Publicis Worldwide
AND
ETHICS

2. WHAT IS THIS?
‣ Advertisers and ethics… WTF!
‣ What me ethical?
‣ Mapping the code.
‣ Why do this at all?

3. WHAT IS THIS NOT?
‣ An attempt to get you to Tweet about something
‣ A vision for Tim’s perfect future
‣ A shameless plug for any association, business
or way of thinking

4. THAT BEING SAID, STICK AROUND
AND GET YOUR MIND BLOWN

5. WHY DOES ADVERTISING
CARE?

6. ADVERTISING SPENDS THE MONEY
“Follow the money.” -Karl Marx

7. AND IT’S A LOT...
=
2015 GDP
Portugal
Vietnam
Czech Republic
198 billion
199 billion
182 billion
579 billion
IMF - World Bank

8. https://blog.pagefair.com/2015/ad-blocking-report/
BUT WE HAVE A LOT TO LOSE

9. Brad Frost - Death to Bullshit
AND WE ALSO NEED TO RETHINK
OUR METHODS

10. BUT DON’T FEAR – WE HAVE
DATA AND DATA SCIENTISTS!

11. WHAT IS A DATA SCIENTIST?
‣ Statistics
‣ Data Strategy
‣ Social Science
‣ Coding chops
‣ Good Looks

12. AND WE SEEM TO HAVE MORE AND MORE
OF THEM IN THE WORLD IN GENERAL

13. O’Riley 2015 Data Science Survey
http://duu86o6n09pv.cloudfront.net/reports/2015-data-science-salary-survey.pdf
of +/- 600 respondents
1%
9%
23%
25%
14%
13%
6%
5%
4%
0%
5%
10%
15%
20%
25%
30%
<21 21+25 26+30 31+35 36+40 41+45 46+50 51+55 56<
Percent2of2Respondents
Reported2 Age
THEY ARE ALSO A YOUNG BUNCH

14. AND THAT MAKES SENSE AS
IT IS A YOUNG PROFESSION
1996 Members of the
International Federation of
Classification Societies (IFCS)
meet in Kobe, Japan.
2001 William S. Cleveland
publishes “Data Science: An Action
Plan for Expanding the Technical
Areas of the Field of Statistics.”
FIRST USE OF
“DATA SCIENCE”
THE PAPER THAT
LAUNCHED A 1,000 NERDS

15. MOREOVER, NEW ENTRANTS INTO THE
FIELD ARE NOT GIVEN VERY MUCH
ETHICAL TRAINING
Surveyed Syllabi from 13 Intro to Data Science Courses

16. ONLY THREE HAVE AT LEAST ONE
MENTION OF AN “ETHICS” COMPONENT
IN THE SYLLABUS

17. REGARDLESS, DATA SCIENCE IS
AFFECTING ALL OF OUR EVERYDAY
LIVES… OUR ONLINE LIVES

18. OUR MOVEMENT…

19. OUR MEDIA…

20. OUR MILITARY…

21. OUR POLITICS…

22. EVEN OUR IPHONES...
– Tim Cook

23. Earl, I think Data
Science needs a code
of ethics.
Yup.

24. A CODE OF ETHICS WOULD
‣ Establish credibility and responsibility outside
of nerd-dom
‣ Provide a starting point to act as technology
changes
‣ Galvanize the disparate data practitioner
community

25. ALL THAT’S FINE…

26. BUILD ANYTHING YOU FIRST HAVE
UNDERSTAND WHAT YOU ARE
WORKING WITH

27. A crash course in codes of ethics:
THAT SHIT HUMANS DO

28. A TIMELINE OF ETHICAL CODES
EGYPTIAN
CODE OF
MA’AT
JEWISH
TORAH
HIPPOCRATIC
OATH
BUSHIDO
WARRIOR
CODE
PIRATE’S
CODE OF THE
BRETHREN
FRENCH
FOREIGN
LEGION CODE
D'HONNEUR
JOURNALIST’S
CREED
NUREMBURG
CODE
I.R.B. - EXEMPT
COMMON RULE
INTERNATIONAL
STATISTICAL
INSTITUTE
ASSOCIATION
FOR COMPUTING
MACHINERY
AMERICAN
STATISTICAL
ASSOCIATION
DRAFT MODEL
BIOETHICISTS
CODE
~1200 bce~2300 bce ~500 bce 1914~1600
~1000 1831
1999199219811946
1985
2005
increase of professional codes

29. ETHICAL CODES ARE NOT ALL THE SAME
BUT THEY HAVE TWO CLASSES OF
CHARACTERISTICS
Inward
facing goals
Outward
facing goals

30. INWARD FACING GOALS
‣ Provide guidance when norms are not
explicit
‣ Reduce internal conflicts and build a
common purpose
‣ Establish professional behavior
‣ Deter unethical behavior with sanctions and
internal reporting structures

31. OUTWARD FACING GOALS
‣ Protect vulnerable populations who could be
harmed by profession’s activities
‣ Establish the profession as a distinct moral
community worthy of autonomy
‣ Serve as tool for disputes between member
and non-member parties
‣ Create institutions resilient to external
pressures

32. PROMOTE POSITIVE ENFORCEMENT
‣ Accept the distributed nature of
professional communities creates too many
judicial problems for active regulation
‣ Construct the code with consensus
allowing for broad buy-in
‣ Set boundaries and expectations of the
practicing community, allowing for self-
affirming social control mechanisms

33. ‣ Mediate internal group needs and external
community interactions
‣ Adapt to future unknown circumstances
‣ Inspire collective identity supporting
adherence and adoption
OVERALL A PROFESSIONAL
CODE OF ETHICS SHOULD:

34. OKAY PROFESSOR, SO WHAT IS THE
REAL REASON DATA SCIENCE NEEDS
AN ETHICAL CODE?

35. MORAL HAZARD

36. "In economics, moral hazard occurs
when one person takes more risks
because someone else bears the
burden of those risks."
– wikipedia
https://en.wikipedia.org/wiki/Moral_hazard

37. MORAL HAZARD IN LENDING

38. http://www.pnhp.org/facts/single-payer-resources
MORAL HAZARD IN HEALTH CARE

39. http://www.economist.com/news/world-week/21569742-kals-cartoon
MORAL HAZARD IN ARMAMENTS

40. ‣ Connections between data and the people
it represents are very abstracted
‣ Digital creations affect people we never
see
‣ Unintended algorithmic consequences are
almost never known or explored
‣ When was the last time an algorithm ever
“hurt” anybody?
DATA SCIENCE IS STEEPED IN
MORAL HAZARD

41. Well, shit.

42. HOW A DATA SCIENCE CODE
MAY BEGIN TO LOOK

43. –Paul Ohm
“Broken Promises of Privacy: Responding to
the Surprising Failure of Anonymization,”
UCLA Law Review 57,p.1702
“Data can be useful
or anonymous,
but never both.”

44. THUS A CODE WOULD NEED
TO MAINTAIN THE UTILITY
OF DATA
WHILE BALANCING
CONTROL OF THAT DATA

45. A FRAMEWORK FOR A CODE IS
COMPOSED OF THREE CLUSTERS
Data Ethics Code
Safety of used
data & analysis
Protection of
subjects
Mathematical
responsibility
Community
Privacy
bio-
information
Business
applications
3rd party
usage
Identity
Ownership Verification
Right to be
forgotten
Incorrect data
correction

46. PRIVACY
‣ Once you buy or sell data what are the ethics around
using it? You did ‘buy it’ right?
3rd party data
‣ What is the relationship between privacy of internet
exploration and advertisement of relevant
products?
Business applications
‣ Is data generated from your body owned differently?
Bio-information

47. COMMUNITY
‣ How do we protect people who our analysis affects
for negative consequences?
Protection of subjects
‣ Is there a system for correct use of professional
tools and continuing education?
Mathematical responsibility
‣ Once data is used how is it discarded and sensitive
analysis protected?
Safety of used data & analysis

48. IDENTITY
‣ Is there a need for a centralized personal data
safe?
Ownership
‣ How do means of validation affect access, privacy and
safety?
Validation
‣ What are the mechanisms to correct bad data?
Incorrect data correction

49. THESE COMPONENTS PROVIDE THE
BASIS FOR CONVERSATION NOT A
HARD STRUCTURE
Data Ethics Code
Identity
Safety of used
data & analysis
Protection of
subjects
Mathematical
responsibility
Community
Privacy
bio-
information
Business
applications
3rd party
usage
Ownership Verification
Right to be
forgotten
Incorrect data
correction

50. ARE THERE OTHER THINGS
WE SHOULD THINK ABOUT?

51. The code can not
be built on
personal
conceptions of
right and wrong.
It must be general
enough to span
cultures,
companies and
continents.

52. THE CODE SHOULD EXIST OUTSIDE
ANY FORMAL BUSINESS.
YOU SHOULD NOT MAKE MONEY OFF
THE CODE.

53. The code should not be created
by a small group, but rather
presents a chance for a more
radical form of democracy

54. Whatever the
combination, the code
will have to be built by
data scientists to have
any chance at adoption

55. Often ethical codes
come up after social
disasters, can we get
out in front of this?

56. Other than it
could be good
for people, why
do this at all?

57. IT MAKES GOOD
BUSINESS SENSE

58. More ethical data
treatment lowers
liability and
reduces
corporate risk

59. Its not a matter of if you get
hacked it is a matter of when
(and frankly if you find out)

60. http://www.techrepublic.com/article/data-breaches-may-cost-less-than-the-security-to-prevent-them/
$252 MILLION DOLLARS
2013 - data breach

61. ESTIMATED $100 MILLION - $500 MILLION
2006 - data theft
http://www.lifehealthpro.com/2015/06/18/the-10-most-expensive-data-breaches?t=regulatory&slreturn=1456110972&page=5

62. HIGH ESTIMATES $4 BILLION DOLLARS
2011 - data breach of 75 client companies
http://www.eweek.com/c/a/Security/Epsilon-Data-Breach-to-Cost-Billions-in-WorstCase-Scenario-459480
marketing data

63. THE MORAL HIGH GROUND
ALSO SELLS MORE SHIT

65. COVER YOUR ASS

66. PEOPLE WHO ARE CAUGHT
UP IN UNETHICAL BEHAVIOR
ARE USUALLY SACKED

67. THEIR PROJECTS ARE SCRAPPED

68. AND IT GETS UGLY
FROM A PROFESSIONAL
POINT OF VIEW

69. OK, SO WHAT’S NEXT?

70. Some folks working on this:
‣ The Council for Big Data, Ethics and Society
‣ Certified Analytics Professionals
‣ Michael McFarland, S.J. - Computer Scientist
‣ Cynthia Dwork - Microsoft Research
‣ Kord Davis - Digital Strategist
READ MORE HERE

71. TALK AMONGST YOUR FRIENDS

72. I’LL GIVE YOU A TOPIC

73. The right to be forgotten
an ideal or practically achievable?

74. It seems data is a commodity
does that make the data we create a
personal asset?

75. Ethical in a data decision making sense?
Edward Snowden

76. WHO IS LOOKING AFTER
YOUR DATA?

77. THANK YOU