Introduction to Ethics of Big Data

An Introduction to
Ethics of Big Data
 Webcast by Kord Davis

Preamble
 Deeply passionate about more productive discussion
 Academic background in philosophy
 Experiences with Big Data
 Topic site to launch with book release
 #ethicsofbigdata

www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata

Current State
 A data architect and a product
manager (or two) walk into a
meeting…
 Creepy? Yes, it is. No, it isn’t.
 In the absence of a common
vocabulary and framework for
discussion, individuals revert to
their own moral codes


Future State
 Creating a common vocabulary and
framework for productive discussion
(ethical inquiry)
 Ethics of Big Data is about
understanding the influence of our
values on our actions.
 Seeking a set of common values
 Action in alignment with those values


Path Forward
 Review key concepts in the book
 Raise awareness and engagement
 Intentional ethical inquiry
 Explore a framework
» Four Aspects of Big Data Ethics
» Ethical Decision Points
» Values & Actions Alignment
» Value Personas


What Is This Big Data Thing?
 Assuming familiarity
 Some characteristics
 Familiar examples
 Volume, variety, velocity creates a “forcing function”


What Is This Ethics Thing?
 Big Data is ethically neutral
 Use of Big Data is not
 Ethics is only one aspect
 Abstract concepts, real implications
 Ethical inquiry informs ethical action
 Ethical principles informed by values
 Aligning action with those values


Big Data’s Forcing Function
 Pushing business operations
deeper into our lives
 Changing common meanings
 Value implications

 Volume, Variety, Velocity
 The Supreme Court and Netflix
 Data Exhaust/Ecosystem/Life stream
 Meta-data


Anatomy of a Tweet
 Place type
 Verified Badge status
 Number of favorites
 Number of followers
 Protected status
 Country
 Application used to Tweet
 Author’s screen name
 Author’s biography


 Imagine if Hemingway blogged, JFK tweeted, or Rosa
Parks was on Facebook
 What will our grandchildren know about us that we didn’t
know about our grandparents?
 Library of Congress
 Political change (Egypt, SOPA)
 Social change (location, interaction, communication)
 Education, Healthcare, Weather


 Risk
» Security
» Privacy
» Legal compliance
» Customer Engagement
 Opportunity
» Innovation
» Deeper insights
» Broader outlooks
» Customer Engagement


Balancing Risk and Innovation
 Use the Force
» Acknowledge
» Frame
» Differentiate
» Engage
 Frame Key Aspects


Four Aspects of Big Data Ethics
 Identity
 Privacy
 Ownership
 Reputation


Identity Privacy

Ownership Reputation


 Identity
» Is offline existence identical to online existence?
 Privacy
» Who should control access to data about you?
 Ownership
» What does it mean to own data about ourselves?
 Reputation
» How can we determine what is trust-worthy?


Identity Privacy

Identity Ownership Reputation

 “Identity is prismatic”, Chris Poole
 “Having two identities for yourself is an example of a
lack of integrity”, Mark Zuckerberg
 Fred Wilson proposes “lightweight” online identity
 New products and services launch
 US Government proposes National Strategy for
Trusted Identities in Cyberspace
 Google+ policy changes on pseudonyms


Identity Privacy

Privacy Ownership Reputation

 “Data can be either useful or perfectly anonymous but
never both.” (Paul Ohm)
 87% of Americans can be identified by three data
points: gender, birthdate, ZIP code
 In last 12 months:
» Supreme Court ruled warrantless GPS tracking is unconstitutional
» Homeland Security tests crime prediction technology
» Zuckerberg declares “age of privacy” to be over
» New product and services launch based on privacy control
» O’Reilly publishes “Privacy and Big Data”
» Google streamlines 60+ privacy policies into one—generating
industry debate and Congressional inquiry


Identity Privacy

Ownership Ownership Reputation

 Google website devoted to “liberating your data”
 UK government proposes “personal identity data”
marketplace (a DC company does the same)
 Doc Searls calls for an end to collecting customer data
 World Economic Forum (WEF) describes personal data
as a “new economic asset class” (data as currency)
 The Sierra Club sues Orange County for access to GIS
data


Identity Privacy

Reputation Ownership Reputation

 Google+ policy changes on pseudonyms
 “The Future of Reputation” (2007)
 New products and services launch
 “Reputation management”
 Ongoing debate about reputation
» It’s alive: http://www.avc.com/a_vc/2010/03/how-to-defend-your-reputation.html
» It’s dead: http://techcrunch.com/2010/03/28/reputation-is-dead-its-time-to-overlook-our-indiscretions/


Current Practices: Alignment?
 What is the current state of affairs?
 Fortune 50 public-facing policy
statements

Current Practices: Buying & Selling
 On the one hand…34 out of Fortune 50 companies
would NOT SELL personal data without consent
 No policy stated they would sell personal data

 On the other hand…11 out of Fortune 50 stated they
would BUY (or “obtain”) data from third parties
 0 out of Fortune 50 stated they would NOT BUY
personal data


Ethical Decision Points
 If it’s not okay to sell something,
how is it okay to buy it?

 Ethical incoherence
 Values and actions can be in conflict
 “Don’t know what we don’t know”
 Opportunity to align values and
actions to increase collaborative
innovation
 Reduce value conflicts in Big Data
innovations
 Comfort factor (reduced creepy)


 Some real-world examples include:
» adding a new product feature
» policy development
» security breach
» designing new products/services
» opportunity to use or combine data in a new way


 Continuous cycle:
» Inquiry Inquiry
» Understanding
» Articulation
Action Understanding
» Action

Articulation


Values & Actions
 Alignment
 Benefits of alignment
 Risks of misalignment
 Value Personas


Values & Actions
 You already know how to do this
 Our values inform our actions all the time
 We value lots of things
 Ethical practice is an outcome of ethical inquiry
 Ethical inquiry is an exploration of values
 Values can be personified


Value Personas
 Evolution of “user persona”
 Articulate how specific values inform
specific actions in specific context
 Aligns operational perspectives on
values & actions:
executives, managers, line staff
 Designed in collaborative workshops
with cross-functional team


Value Personas
 Considerations include:
» Intention
» Security
» Likelihood
» Aggregation
» Responsibility
» Benefit
» Harm
» Organizational roles
» Timeline


Common Themes
 Big Data is:
» Ubiquitous
» Permanent
» Explicit
» Aggregated


Transparent Collaboration
 Acknowledge the complexity at Ethical Decision Points
 Be explicit with ethical discussions
 Engage the nuance
 Encourage “value talk”
 Use Value Personas to help create a common
understanding & facilitate dialog
 Seek alignment of values and actions
 See Exact Target best practices
» http://subscribersrule.com/
» http://www.exacttarget.com/company/orange-culture.aspx


Transparency
 If your data handling actions
were known today … would
they align with your values?
Your customer’s values?

Thank You!
 Topic site: www.ethicsofbigdata.com
 Blog: www.kordindex.com
 Email: kord@28burnside.com
 Twitter: http://twitter.com/kordindex
 LinkedIn: http://www.linkedin.com/in/korddavis
 Ethics of Big Data (O’Reilly, March 2012):
http://shop.oreilly.com/product/0636920021872.do

All references will be provided in archived presentation.


Introduction to Ethics of Big Data

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