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Introduction to Ethics of Big Data

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  • 1. An Introduction to Ethics of Big Data  Webcast by Kord Davis
  • 2. Preamble Deeply passionate about more productive discussion Academic background in philosophy Experiences with Big Data Topic site to launch with book release #ethicsofbigdatawww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 3. 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 codeswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 4. 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 valueswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 5. 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 Personaswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 6. What Is This Big Data Thing? Assuming familiarity Some characteristics Familiar examples Volume, variety, velocity creates a “forcing function”www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 7. 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 valueswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 8. Big Data’s Forcing Function Pushing business operations deeper into our lives Changing common meanings Value implications
  • 9. Big Data’s Forcing Function Volume, Variety, Velocity The Supreme Court and Netflix Data Exhaust/Ecosystem/Life stream Meta-datawww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 10. 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 biographywww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 11. Big Data’s Forcing Function 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, Weatherwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 12. Big Data’s Forcing Function Risk » Security » Privacy » Legal compliance » Customer Engagement Opportunity » Innovation » Deeper insights » Broader outlooks » Customer Engagementwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 13. Balancing Risk and Innovation Use the Force » Acknowledge » Frame » Differentiate » Engage Frame Key Aspectswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 14. Four Aspects of Big Data Ethics Identity Privacy Ownership Reputation
  • 15. Four Aspects of Big Data Ethics Identity Privacy Ownership Reputationwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 16. Four Aspects of Big Data Ethics 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?www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 17. Identity PrivacyIdentity 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 pseudonymswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 18. Identity PrivacyPrivacy 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 inquirywww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 19. Identity PrivacyOwnership 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 datawww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 20. Identity PrivacyReputation 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/www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 21. Current Practices: Alignment? What is the current state of affairs? Fortune 50 public-facing policy statements
  • 22. 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 datawww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 23. Ethical Decision Points If it’s not okay to sell something, how is it okay to buy it?
  • 24. Ethical Decision Points 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)www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 25. Ethical Decision Points 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 waywww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 26. Ethical Decision Points Continuous cycle: » Inquiry Inquiry » Understanding » Articulation Action Understanding » Action Articulationwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 27. Values & Actions Alignment Benefits of alignment Risks of misalignment Value Personaswww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 28. 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 personifiedwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 29. 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 teamwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 30. Value Personas Considerations include: » Intention » Security » Likelihood » Aggregation » Responsibility » Benefit » Harm » Organizational roles » Timelinewww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 31. Common Themes Big Data is: » Ubiquitous » Permanent » Explicit » Aggregatedwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 32. 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.aspxwww.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata
  • 33. Transparency If your data handling actions were known today … would they align with your values? Your customer’s values?
  • 34. 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.doAll references will be provided in archived presentation.www.ethicsofbigdata.com Twitter: @kordindex #ethicsofbigdata

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