Confessions of a “Recovering” Data Broker: Responsible Innovation in the Age of Big Data, Big Brother, and the Coming Skynet Terminators

1. Confessions of a "Recovering" Data Broker Responsible Innova.on in the Age of Big Data and Big Brother Jim Adler Vice President, Products Metanau.x jimadler@metanau.x.com @jim_adler hDp://jimadler.me Markkula Center for Applied Ethics Feb 25 2014

2. Plea “Can’t we all just get along?” − Rodney King Geeks High-‐Tech Mercenary Social Entrepreneur Responsible Innovator Suits Tradi?onal Capitalist Wonks

3. Lesson Eclectic generalists drive innovation. Richard Feynman Quantum Physics Steve Jobs ‘nuﬀ said Stephen Hawking Cosmology Norio Ohga Sony President 74 min CD Temple Grandin Animal Handling

4. Confession I am not an attorney. Intelligence Geek Obsession Dweeb Nerd Dork Social Inep.tude

5. Confession You can often do more good from the inside than the outside. •  Founded in 2003 •  20B public records •  30M visitors per month •  50M+ reports sold

6. Confession The “public” data supply chain of you Payments Civil Suits Criminal Records Commercial Risk Resumes Government Public Posts Names Addresses Blogs Search Phone Numbers Collection Self-Reported Big Data Engines Marke.ng Directory Background Use

7. Confession We don’t know you all that well. Billions of Records Millions of People Philip Collins 375 People Jim Adler 213 Records 37 People Carol Brooks 9800 Records 1250 People Randolph Hutchins 5 People Gwen Fleming 2 People 213 records linked to the correct 37 Jim Adlers Jim Adler Houston, TX Age 70 Jim Adler McKinney, TX Age 57 Jim Adler Has.ngs, NE Age 32 Jim Adler Canaan, NH Age 59 Jim Adler Redmond, WA Age 50 Jim Adler Denver, CO Age 48

8. Confession BANKING SERVICES Lots of uses for your data … some regulated. CALLER ID OF HARASSING PHONE CALLS ONLINE SHOPPERS VERIFYING ONLINE SELLERS LEARNING ABOUT ADOPTED KIDS SEEKING THEIR BIOLOGICAL PARENTS A BUSINESS SOCIAL NETWORKERS LOOKING TO EXPAND THEIR FRIENDS LIST NON-‐PROFIT ORGANIZATIONS LOOKING FOR SUPPORTERS CHECKING OUT A PROSPECTIVE TENANT THOSE IN LEGALLY ENTANGLED LOOKING FOR COURT RECORDS RESEARCHING A PROSPECTIVE EMPLOYEE ALUMNI GROUPS ARRANGING REUNIONS NETWORKERS SEEKING BUSINESS OPPORTUNITIES GENEALOGISTS CULTIVATING THEIR FAMILY TREE ANYONE RETRIEVING COURT RECORDS SHARING FIND OWNER OF DOG’S RELATIVE FOR TRANSPLANT RESEARCH SALES PROFESSIONALS LOOKING FOR NEW PROSPECTS CHECKING OUT A PROSPECTIVE DATE FIANCÉS AND THEIR CURIOUS FAMILY MEMBERS ENFORCEMENT SOCIAL WORKERS WHO NEED TO KNOW MORE ABOUT THEIR CLIENTS LAWYERS NEEDING QUICK ACCESS TO COURT RECORDS BUSINESSES THAT NEED TO UPDATE CONTACT INFORMATION ON CUSTOMERS ANYONE CURIOUS ABOUT WHO'S EMAILING OR CALLING THEM AIRLINES TRYING TO RETURN LOST LUGGAGE PROFESSIONALS LEARNING ABOUT COLLEAGUES AT CONFERENCES SINGLES CURIOUS ABOUT THE PEOPLE THEY MEET INVESTIGATIVE JOURNALISTS RUNNING DOWN LEADS RECONNECTING OUT-‐OF-‐TOUCH FAMILY MEMBERS LAW PARENTS ENSURING WHO THEIR KIDS SAFETY FINDING PEOPLE THAT HAVE THE SAME ILLNESS AS YOU ANYONE WHO NEED ADDRESS HISTORIES FOR PASSPORTS CHECKING OUT A PROSPECTIVE SOCIAL NETWORK CONNECTION FINDING LONG-‐LOST FRIENDS, MILITARY BUDDIES, ROOMMATES, OR CLASSMATES REGULATED

9. Confession Opt-‐out doesn’t always mean deletion. Jane Hampton Jane Hampton Jane Hampton 06/23/1998 123 Main Peoria, IL 123 Main Peoria, IL jane@facebook.com (309)555-8931

10. Lesson Listen to your toughest critics.

11. Lesson Can I have a little narcissism with my voyeurism? •  What does my background check say? •  Privacy controls –  Suppress single address or phone number •  Comment on your own public proﬁle

12. Regulation New Data Broker Bill Introduced This Month •  “Data Broker Accountability and Transparency Act” •  Prohibits “data brokers” from using decep?ve means to collect informa.on about consumers. •  Transparency to consumers about informa.on about them. •  Consumers can correct the data. •  Opt-‐out of having their data collected. •  FTC enforcement

13. Lesson When towns were small, personal anonymity was low … “The only thing worse than being talked about, is not being talked about.” − Oscar Wilde

14. Lesson Urban populations grew along with personal anonymity… “Good Fences Make Good Neighbors” − Robert Frost

15. Confession … we’re suffering from Privacy Vertigo. 120 “Rockwell” Era “Good Fences” Era Privacy Expectations ! 100 “Privacy Ver.go” Era 80 Onli nsity ne De 60 40 20 Urban Density 0 1850 1890 1930 1970 2010

16. In privacy contexts, Power matters. Privacy Rights ! Lesson Peer to Peer Corpora.on & Customer/Employee Government & Ci.zen Power Disparity ! Your God & You

17. Lesson How to unpack Privacy? Think PPP. PERILS

18. Mapping Places-‐Players-‐Perils Cases Private Curtilage Governments Employers/Landlords/Insurers Public M O R E P L A Y E R P O W E R G A P Lesson Parents Peers M O R E P R I V A T E P L A C E S

19. Lesson Places-‐Players-‐Perils Cases M O R E P L A Y E R P O W E R G A P US deports Bri?sh tourists over Tweets NSA internet ci?zen surveillance Georgia teacher FBI GPS criminal fired aXer pos?ng Google privacy surveillance vaca?on pics policy unifica?on "Girls Around Me" pulled from market Ethically Challenging Target finds out News of the World teen pregnant Health orgs use phone hacking before parents Twi[er to track illness Actress sues IMDB over revealing her age FB user sets fire to home aXer de-‐ friending GM OnStar tracks users M O R E P R I V A T E P L A C E S Woman caught naked by Google Street View Rutgers student commits suicide aXer spied by webcam

20. M O R E P L A Y E R P O W E R G A P Confession Big brother is watching (duh). NSA internet “We’re being asked to trust US deports Bri?sh ci?zen without being able to verify.” tourists over Tweets surveillance − Alex Howard (big data journalist) GPS criminal Georgia teacher FBI fired aXer pos?ng Google privacy vaca?on pics policy unifica?on surveillance Target finds out News of the World teen pregnant Health orgs use phone hacking before parents Twi[er to track illness Pres. Obama calls for more transparency in FISA court and Woman caught Actress sues IMDB surveillance laws naked by Google "Girls Around Me" pulled from market over revealing her age FB user sets fire to home aXer de-‐ friending GM OnStar tracks users Street View Rutgers student commits suicide aXer spied by webcam NSA chief announces plan to replace 1,000 sysadmins with machines M O R E P R I V A T E P L A C E S

21. Lesson Technology grows exponentially. Wisdom grows linearly. •  Gov’t doesn’t trust people (at least sysadmins) but does trust machines •  LiDle Transparency •  Wisdom is hard to come by •  Sen.ent (?) brain in the cloud in < 20 years Wisdom Knowledge Informa.on Data

22. Prediction A head in the clouds < 20 years $100,000 Human Brain •  20,000 TFlops •  2,500 Terabytes More than $325M per year $27,100 $13,500 Cost per Month (000s) $10,000 $6,800 $3,400 Chris Westbury, University of Alberta Less than $700K per year à 4 M AWS m1.large nodes $1,700 $1,000 $850 $420 $210 $100 $100 $53 $26 $13 $10 $7 $3 $2 $1 $1 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 Year

23. Lesson Big data inferences are not thoughtcrimes. “… the essen.al crime that contained all others in itself. Thoughtcrime, they called it.” – George Orwell “Watch your thoughts, they become words. Watch your words, they become ac.ons. Watch your ac.ons, they become habits. Watch your habits, they become your character. Watch your character, it becomes your des.ny.” – Lao Tzu

24. Confession Target knows you’re pregnant and when you’re due. So, what’s so perilous?

25. Confession “To Serve Man” is a cookbook. “If you’re not paying for the product, you are the product.” − Claire Wolfe (paraphrased)

26. Sometimes you’re in a public place when you think you’re in a private place. Confession “Gaydar” A 2009 MIT study found it was possible to predict men’s sexual orientation by analyzing the gender and sexuality of their social network contacts – even if the rest of the information on their proﬁle was set to private.

27. Confession John Foreman’s Excellent Disney Adventure

28. Felon Classiier Sampling Linking 250 M Defendants Cleaning Objec.ve If someone has minor oﬀenses on their criminal record, do they also have felonies? 15K Labels 15K Predictors Feature Extrac.on Learner Model Bloomberg ar.cle: hDp://bloom.bg/1eMtnug

29. How does the Felon Classiier work? Gender Eye Color Ta[oos Male (+0.1) Blue 2 + Female Brown (+1.2) < 2 (+0.1) Green Criminal Offenses Score Over Threshold of 3.5? Likely Felon? Traffic only (-‐0.5) 4 or fewer misdemeanors 8 or fewer misdemeanors YES 0.9 NO NO Hazel Male Female (-‐0.5) Blue 2 + (+1.3) Traffic only Brown < 2 4 or fewer misdemeanors (+1.9) Green 8 or fewer misdemeanors YES 4.4 NO YES Hazel (+1.7) Bloomberg ar.cle: hDp://bloom.bg/1eMtnug Blog widget: hDp://jimadler.me

30. Confession Classiiers depend on policy as much as technology. False Negative Rate A N A R C H Y 100.0% 80.0% 60.0% 40.0% Threshold: 1.1 FP Rate: 1% FN Rate: 40% Threshold: 0.66 FP Rate: 5% FN Rate: 22% Threshold: -‐1.82 FP Rate: 19% FN Rate: 0% 20.0% 0.0% 0.0% 5.0% 10.0% False Positive Rate T Y R A N N Y 15.0% 20.0%

31. Ruling NYC Stop & Frisk Found Unconstitutional 90% of Criminals are Minorities Minorities 50% Criminals “The city … believes that blacks and Hispanics should be stopped at the same rate as their propor.on of the local criminal suspect popula.on.” − US District Judge Shira Scheindlin All NYC Residents

32. Lesson “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” Bayes’ Rule PMinority is a Criminal = Minorities 50% 10% of Criminals are Not Minorities Criminals 5% 90% of Criminals are Minorities PCriminal is a Minority PCriminal PMinority PMinority is a Criminal = 90% 5% = 9% 50% PMinority is NOT a Criminal = 100 − PMinority is a Criminal = 91% All NYC Residents If it’s not ok to stop 99% of the general popula.on for nothing, why is it ok to stop 91% of minori.es for nothing?

33. Lesson When might Stop & Frisk be OK? Bayes’ Rule PMinority is a Criminal = Asians 10% Criminals 5% PCriminal is a Minority PCriminal PMinority PMinority is a Criminal = 90% 5% = 45% 10% PMinority is NOT a Criminal = 100 − PMinority is a Criminal = 55% All NYC Residents If it’s not ok to stop 99% of the general popula.on for nothing, is it ok to stop 55% of minori.es for nothing?

34. Lesson What about Newark’s Stop & Frisk? hDp://www.ny.mes.com/2014/02/25/nyregion/newark-‐ stop-‐and-‐frisk-‐data-‐is-‐analyzed.html Bayes’ Rule Minorities 50% Criminals 20% PMinority is a Criminal = PCriminal is a Minority PCriminal PMinority PMinority is a Criminal = 90% 20% = 36% 50% PMinority is NOT a Criminal = 100 − PMinority is a Criminal = 64% All Newark Residents If it’s not ok to stop 96% of the general popula.on for nothing, is it ok to stop 64% of minori.es for nothing?

35. Lesson Hilary Mason’s Maxim Math + Code = Awesome Quants Making a killing on Wall Street but s.ll can’t impress the chicks Weakonomics.com

36. Lesson Corollary to Mason’s Maxim Values * (Math + Code) = Awesome

37. Prediction “The beatings will continue until morale improves.” Overton Window Unthinkable Acceptable Radical Facebook Newsfeed Ad Targeting Facebook Beacon Pre-crime Skynet Popular Sensible Policy

38. Lesson Living within a Filter Bubble (with apologies to Eli Pariser) … but then we reshape our tools … “We shape our tools … … and thereawer our tools shape us.” − Marshall McLuhan

39. Lesson “No one here gets out alive.” − Jim Morrison Listen, Learn Geeks Suits Wonks Scru.nize, Incen.vize Adapt, Invent

40. Questions? Jim Adler www.metanau.x.com jimadler@metanau.x.com @jim_adler