SlideShare una empresa de Scribd logo
1 de 77
Descargar para leer sin conexión
DATA
Tim Rich
Director of Data Science
Publicis Worldwide
AND
ETHICS
WHAT IS THIS?
‣ Advertisers and ethics… WTF!
‣ What me ethical?
‣ Mapping the code.
‣ Why do this at all?
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
THAT BEING SAID, STICK AROUND
AND GET YOUR MIND BLOWN
WHY DOES ADVERTISING
CARE?
ADVERTISING SPENDS THE MONEY
“Follow the money.” -Karl Marx
AND IT’S A LOT...
=
2015 GDP
Portugal
Vietnam

Czech Republic
198 billion
199 billion
182 billion
579 billion
IMF - World Bank
https://blog.pagefair.com/2015/ad-blocking-report/
BUT WE HAVE A LOT TO LOSE
Brad Frost - Death to Bullshit
AND WE ALSO NEED TO RETHINK
OUR METHODS
BUT DON’T FEAR – WE HAVE
DATA AND DATA SCIENTISTS!
WHAT IS A DATA SCIENTIST?
‣ Statistics
‣ Data Strategy
‣ Social Science
‣ Coding chops
‣ Good Looks
AND WE SEEM TO HAVE MORE AND MORE
OF THEM IN THE WORLD IN GENERAL
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
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
MOREOVER, NEW ENTRANTS INTO THE
FIELD ARE NOT GIVEN VERY MUCH
ETHICAL TRAINING
Surveyed Syllabi from 13 Intro to Data Science Courses
ONLY THREE HAVE AT LEAST ONE
MENTION OF AN “ETHICS” COMPONENT
IN THE SYLLABUS
REGARDLESS, DATA SCIENCE IS
AFFECTING ALL OF OUR EVERYDAY
LIVES… OUR ONLINE LIVES
OUR MOVEMENT…
OUR MEDIA…
OUR MILITARY…
OUR POLITICS…
EVEN OUR IPHONES...
– Tim Cook
Earl, I think Data
Science needs a code
of ethics.
Yup.
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
ALL THAT’S FINE…
BUILD ANYTHING YOU FIRST HAVE
UNDERSTAND WHAT YOU ARE
WORKING WITH
A crash course in codes of ethics:
THAT SHIT HUMANS DO
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
ETHICAL CODES ARE NOT ALL THE SAME
BUT THEY HAVE TWO CLASSES OF
CHARACTERISTICS
Inward
facing goals
Outward
facing goals
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
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
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
‣ 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:
OKAY PROFESSOR, SO WHAT IS THE
REAL REASON DATA SCIENCE NEEDS
AN ETHICAL CODE?
MORAL HAZARD
"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
MORAL HAZARD IN LENDING
http://www.pnhp.org/facts/single-payer-resources
MORAL HAZARD IN HEALTH CARE
http://www.economist.com/news/world-week/21569742-kals-cartoon
MORAL HAZARD IN ARMAMENTS
‣ 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
Well, shit.
HOW A DATA SCIENCE CODE
MAY BEGIN TO LOOK
–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.”
THUS A CODE WOULD NEED
TO MAINTAIN THE UTILITY
OF DATA
WHILE BALANCING
CONTROL OF THAT DATA
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
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
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
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
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
ARE THERE OTHER THINGS
WE SHOULD THINK ABOUT?
The code can not
be built on
personal
conceptions of
right and wrong.


It must be general
enough to span
cultures,
companies and
continents.
THE CODE SHOULD EXIST OUTSIDE
ANY FORMAL BUSINESS.
YOU SHOULD NOT MAKE MONEY OFF
THE CODE.
The code should not be created
by a small group, but rather
presents a chance for a more
radical form of democracy
Whatever the
combination, the code
will have to be built by
data scientists to have
any chance at adoption
Often ethical codes
come up after social
disasters, can we get
out in front of this?
Other than it
could be good
for people, why
do this at all?
IT MAKES GOOD
BUSINESS SENSE
More ethical data
treatment lowers
liability and
reduces
corporate risk
Its not a matter of if you get
hacked it is a matter of when
(and frankly if you find out)
http://www.techrepublic.com/article/data-breaches-may-cost-less-than-the-security-to-prevent-them/
$252 MILLION DOLLARS
2013 - data breach
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
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
THE MORAL HIGH GROUND
ALSO SELLS MORE SHIT
COVER YOUR ASS
PEOPLE WHO ARE CAUGHT
UP IN UNETHICAL BEHAVIOR
ARE USUALLY SACKED
THEIR PROJECTS ARE SCRAPPED
AND IT GETS UGLY
FROM A PROFESSIONAL
POINT OF VIEW
OK, SO WHAT’S NEXT?
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
TALK AMONGST YOUR FRIENDS
I’LL GIVE YOU A TOPIC
The right to be forgotten
an ideal or practically achievable?
It seems data is a commodity
does that make the data we create a
personal asset?
Ethical in a data decision making sense?
Edward Snowden
WHO IS LOOKING AFTER
YOUR DATA?
THANK YOU

Más contenido relacionado

La actualidad más candente

A Tutorial to AI Ethics - Fairness, Bias & Perception
A Tutorial to AI Ethics - Fairness, Bias & Perception A Tutorial to AI Ethics - Fairness, Bias & Perception
A Tutorial to AI Ethics - Fairness, Bias & Perception Dr. Kim (Kyllesbech Larsen)
 
Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it? Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it? University of Minnesota, Duluth
 
Introduction to the ethics of machine learning
Introduction to the ethics of machine learningIntroduction to the ethics of machine learning
Introduction to the ethics of machine learningDaniel Wilson
 
Responsible AI
Responsible AIResponsible AI
Responsible AINeo4j
 
The Ethics of Artificial Intelligence
The Ethics of Artificial IntelligenceThe Ethics of Artificial Intelligence
The Ethics of Artificial IntelligenceKarl Seiler
 
AIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAnimesh Singh
 
Generative AI Risks & Concerns
Generative AI Risks & ConcernsGenerative AI Risks & Concerns
Generative AI Risks & ConcernsAjitesh Kumar
 
Bias in Artificial Intelligence
Bias in Artificial IntelligenceBias in Artificial Intelligence
Bias in Artificial IntelligenceNeelima Kumar
 
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Algorithmic Bias:  Challenges and Opportunities for AI in HealthcareAlgorithmic Bias:  Challenges and Opportunities for AI in Healthcare
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
 
How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?Mark Borg
 
Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Krishnaram Kenthapadi
 
Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?University of Minnesota, Duluth
 
Technology for everyone - AI ethics and Bias
Technology for everyone - AI ethics and BiasTechnology for everyone - AI ethics and Bias
Technology for everyone - AI ethics and BiasMarion Mulder
 
Ethical Issues in Machine Learning Algorithms. (Part 1)
Ethical Issues in Machine Learning Algorithms. (Part 1)Ethical Issues in Machine Learning Algorithms. (Part 1)
Ethical Issues in Machine Learning Algorithms. (Part 1)Vladimir Kanchev
 
Artificial Intelligence Introduction & Business usecases
Artificial Intelligence Introduction & Business usecasesArtificial Intelligence Introduction & Business usecases
Artificial Intelligence Introduction & Business usecasesVikas Jain
 
Predictive Analytics - Big Data & Artificial Intelligence
Predictive Analytics - Big Data & Artificial IntelligencePredictive Analytics - Big Data & Artificial Intelligence
Predictive Analytics - Big Data & Artificial IntelligenceManish Jain
 

La actualidad más candente (20)

A Tutorial to AI Ethics - Fairness, Bias & Perception
A Tutorial to AI Ethics - Fairness, Bias & Perception A Tutorial to AI Ethics - Fairness, Bias & Perception
A Tutorial to AI Ethics - Fairness, Bias & Perception
 
Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it? Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it?
 
Introduction to the ethics of machine learning
Introduction to the ethics of machine learningIntroduction to the ethics of machine learning
Introduction to the ethics of machine learning
 
Responsible AI
Responsible AIResponsible AI
Responsible AI
 
[REPORT PREVIEW] The Age of AI
[REPORT PREVIEW] The Age of AI[REPORT PREVIEW] The Age of AI
[REPORT PREVIEW] The Age of AI
 
The Ethics of Artificial Intelligence
The Ethics of Artificial IntelligenceThe Ethics of Artificial Intelligence
The Ethics of Artificial Intelligence
 
AIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AI
 
Generative AI Risks & Concerns
Generative AI Risks & ConcernsGenerative AI Risks & Concerns
Generative AI Risks & Concerns
 
The Ethics of AI
The Ethics of AIThe Ethics of AI
The Ethics of AI
 
Bias in Artificial Intelligence
Bias in Artificial IntelligenceBias in Artificial Intelligence
Bias in Artificial Intelligence
 
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Algorithmic Bias:  Challenges and Opportunities for AI in HealthcareAlgorithmic Bias:  Challenges and Opportunities for AI in Healthcare
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
 
How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?
 
Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)
 
Implementing Ethics in AI
Implementing Ethics in AIImplementing Ethics in AI
Implementing Ethics in AI
 
Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?
 
Technology for everyone - AI ethics and Bias
Technology for everyone - AI ethics and BiasTechnology for everyone - AI ethics and Bias
Technology for everyone - AI ethics and Bias
 
Ethical Issues in Machine Learning Algorithms. (Part 1)
Ethical Issues in Machine Learning Algorithms. (Part 1)Ethical Issues in Machine Learning Algorithms. (Part 1)
Ethical Issues in Machine Learning Algorithms. (Part 1)
 
Artificial Intelligence Introduction & Business usecases
Artificial Intelligence Introduction & Business usecasesArtificial Intelligence Introduction & Business usecases
Artificial Intelligence Introduction & Business usecases
 
Predictive Analytics - Big Data & Artificial Intelligence
Predictive Analytics - Big Data & Artificial IntelligencePredictive Analytics - Big Data & Artificial Intelligence
Predictive Analytics - Big Data & Artificial Intelligence
 
Introduction to AI Ethics
Introduction to AI EthicsIntroduction to AI Ethics
Introduction to AI Ethics
 

Similar a Data Ethics Code Framework for Data Scientists

Opportunities for you, your company and your world
Opportunities for you, your company and your worldOpportunities for you, your company and your world
Opportunities for you, your company and your worldCartegraph
 
Analytic opportunities for you, companies and the world
Analytic opportunities for you, companies and the worldAnalytic opportunities for you, companies and the world
Analytic opportunities for you, companies and the worldTim Suther
 
Digit Leaders 2023
Digit Leaders 2023 Digit Leaders 2023
Digit Leaders 2023 Ray Bugg
 
Opportunities with data science
Opportunities with data scienceOpportunities with data science
Opportunities with data scienceAshiq Rahman
 
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)Adam Thierer
 
Iot privacy vs convenience
Iot privacy vs  convenienceIot privacy vs  convenience
Iot privacy vs convenienceDon Lovett
 
Ai morality-today-2018-web
Ai morality-today-2018-webAi morality-today-2018-web
Ai morality-today-2018-webTom Daly
 
“Permissionless Innovation” & the Clash of Visions over Emerging Technologies
“Permissionless Innovation” & the Clash of Visions over Emerging Technologies“Permissionless Innovation” & the Clash of Visions over Emerging Technologies
“Permissionless Innovation” & the Clash of Visions over Emerging TechnologiesAdam Thierer
 
Technology in Business Law by Ammar Younas
Technology in Business Law by Ammar YounasTechnology in Business Law by Ammar Younas
Technology in Business Law by Ammar YounasAmmar Younas
 
AI and Robotics policy overview - Adam Thierer (Aug 2022)
AI and Robotics policy overview - Adam Thierer (Aug 2022)AI and Robotics policy overview - Adam Thierer (Aug 2022)
AI and Robotics policy overview - Adam Thierer (Aug 2022)Adam Thierer
 
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...Cognizant
 
The First of Me! Insights from the Future of Digital at SxSW 2019
The First of Me! Insights from the Future of Digital at SxSW 2019The First of Me! Insights from the Future of Digital at SxSW 2019
The First of Me! Insights from the Future of Digital at SxSW 2019Inês Almeida
 
The Future of Innovation of Policy - Adam Thierer - Mercatus Center
The Future of Innovation of Policy - Adam Thierer - Mercatus CenterThe Future of Innovation of Policy - Adam Thierer - Mercatus Center
The Future of Innovation of Policy - Adam Thierer - Mercatus CenterAdam Thierer
 
Evolution of Social Media and its effects on Knowledge Organisation
Evolution of Social Media and its effects on Knowledge OrganisationEvolution of Social Media and its effects on Knowledge Organisation
Evolution of Social Media and its effects on Knowledge OrganisationCollabor8now Ltd
 

Similar a Data Ethics Code Framework for Data Scientists (20)

Internet of things
Internet of thingsInternet of things
Internet of things
 
Opportunities for you, your company and your world
Opportunities for you, your company and your worldOpportunities for you, your company and your world
Opportunities for you, your company and your world
 
Analytic opportunities for you, companies and the world
Analytic opportunities for you, companies and the worldAnalytic opportunities for you, companies and the world
Analytic opportunities for you, companies and the world
 
Digit Leaders 2023
Digit Leaders 2023 Digit Leaders 2023
Digit Leaders 2023
 
Artificial intelligence - Digital Readiness.
Artificial intelligence - Digital Readiness.Artificial intelligence - Digital Readiness.
Artificial intelligence - Digital Readiness.
 
Opportunities with data science
Opportunities with data scienceOpportunities with data science
Opportunities with data science
 
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)
Permissionless Innovation and the Future of Tech Policy (Thierer - Oct 2017 ed)
 
SFScon 22 - Paolo Pinto - Real Life Data Anonymization.pdf
SFScon 22 - Paolo Pinto - Real Life Data Anonymization.pdfSFScon 22 - Paolo Pinto - Real Life Data Anonymization.pdf
SFScon 22 - Paolo Pinto - Real Life Data Anonymization.pdf
 
Iot privacy vs convenience
Iot privacy vs  convenienceIot privacy vs  convenience
Iot privacy vs convenience
 
Ai morality-today-2018-web
Ai morality-today-2018-webAi morality-today-2018-web
Ai morality-today-2018-web
 
“Permissionless Innovation” & the Clash of Visions over Emerging Technologies
“Permissionless Innovation” & the Clash of Visions over Emerging Technologies“Permissionless Innovation” & the Clash of Visions over Emerging Technologies
“Permissionless Innovation” & the Clash of Visions over Emerging Technologies
 
Privacy, Emerging Technology, and Information Professionals
Privacy, Emerging Technology, and Information ProfessionalsPrivacy, Emerging Technology, and Information Professionals
Privacy, Emerging Technology, and Information Professionals
 
Technology in Business Law by Ammar Younas
Technology in Business Law by Ammar YounasTechnology in Business Law by Ammar Younas
Technology in Business Law by Ammar Younas
 
S0-Stephen.pptx
S0-Stephen.pptxS0-Stephen.pptx
S0-Stephen.pptx
 
AI and Robotics policy overview - Adam Thierer (Aug 2022)
AI and Robotics policy overview - Adam Thierer (Aug 2022)AI and Robotics policy overview - Adam Thierer (Aug 2022)
AI and Robotics policy overview - Adam Thierer (Aug 2022)
 
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
 
The First of Me! Insights from the Future of Digital at SxSW 2019
The First of Me! Insights from the Future of Digital at SxSW 2019The First of Me! Insights from the Future of Digital at SxSW 2019
The First of Me! Insights from the Future of Digital at SxSW 2019
 
The Future of Innovation of Policy - Adam Thierer - Mercatus Center
The Future of Innovation of Policy - Adam Thierer - Mercatus CenterThe Future of Innovation of Policy - Adam Thierer - Mercatus Center
The Future of Innovation of Policy - Adam Thierer - Mercatus Center
 
Evolution of Social Media and its effects on Knowledge Organisation
Evolution of Social Media and its effects on Knowledge OrganisationEvolution of Social Media and its effects on Knowledge Organisation
Evolution of Social Media and its effects on Knowledge Organisation
 
iotevent
ioteventiotevent
iotevent
 

Último

Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...shambhavirathore45
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxolyaivanovalion
 
Edukaciniai dropshipping via API with DroFx
Edukaciniai dropshipping via API with DroFxEdukaciniai dropshipping via API with DroFx
Edukaciniai dropshipping via API with DroFxolyaivanovalion
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightCheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightDelhi Call girls
 
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...amitlee9823
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxolyaivanovalion
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfRachmat Ramadhan H
 
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...amitlee9823
 
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Valters Lauzums
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysismanisha194592
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxolyaivanovalion
 
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxBPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxMohammedJunaid861692
 
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAroojKhan71
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxJohnnyPlasten
 

Último (20)

Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptx
 
Edukaciniai dropshipping via API with DroFx
Edukaciniai dropshipping via API with DroFxEdukaciniai dropshipping via API with DroFx
Edukaciniai dropshipping via API with DroFx
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023
 
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightCheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
 
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...
Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptx
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
 
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in  KishangarhDelhi 99530 vip 56974 Genuine Escort Service Call Girls in  Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
 
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...
Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...
 
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts ServiceCall Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
 
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptx
 
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxBPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
 
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec
Abortion pills in Doha Qatar (+966572737505 ! Get CytotecAbortion pills in Doha Qatar (+966572737505 ! Get Cytotec
Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptx
 

Data Ethics Code Framework for Data Scientists

  • 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
  • 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
  • 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
  • 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
  • 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?
  • 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
  • 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
  • 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?
  • 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)
  • 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
  • 64.
  • 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
  • 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?