Confessions of a “Recovering” Data Broker: Responsible Innovation in the Age of Big Data, Big...

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 [email protected] @jim_adler hDp://jimadler.me

Markkula Center for Applied Ethics Feb 25 2014

“Can’t we all just get along?” Plea

Geeks

Wonks Suits

Social Entrepreneur

High-‐Tech Mercenary

Responsible Innovator

Tradi?onal Capitalist

− Rodney King

Norio Ohga Sony President 74 min CD

Steve Jobs ‘nuff said

Eclectic generalists drive innovation. Lesson

Temple Grandin Animal Handling

Stephen Hawking Cosmology

Richard Feynman Quantum Physics

I am not an attorney. Confession

Intelligence

Social Inep.tude Obsession Dork

Nerd Geek Dweeb

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

Confession

The “public” data supply chain of you Confession

Government

Commercial

Self-Reported

Risk

Marke.ng Directory

Background

Use

Search

Blogs

Criminal Records

Civil Suits

Addresses Phone

Numbers

Payments Resumes

Public Posts Names

Collection

Big Data Engines

Billions of Records Millions of People

Jim Adler Houston, TX

Age 70

Jim Adler Redmond, WA

Age 50 Jim Adler Denver, CO Age 48

Jim Adler McKinney, TX

Age 57

Jim Adler Canaan, NH Age 59

Jim Adler Has.ngs, NE

Age 32

213 records linked to the correct 37 Jim Adlers

Philip Collins

375 People Jim Adler 213 Records 37 People

Randolph Hutchins 5 People

Gwen Fleming 2 People Carol Brooks

9800 Records 1250 People

Confession We don’t know you all that well.

FIND OWNER OF DOG’S RELATIVE FOR TRANSPLANT

SINGLES CURIOUS ABOUT THE PEOPLE THEY MEET

PARENTS ENSURING WHO THEIR KIDS SAFETY

GENEALOGISTS CULTIVATING THEIR FAMILY TREE

BUSINESSES THAT NEED TO UPDATE CONTACT INFORMATION ON CUSTOMERS

FINDING LONG-‐LOST FRIENDS, MILITARY BUDDIES, ROOMMATES, OR CLASSMATES

ANYONE CURIOUS ABOUT WHO'S EMAILING OR CALLING THEM

THOSE IN LEGALLY ENTANGLED LOOKING FOR COURT RECORDS ANYONE WHO NEED ADDRESS

HISTORIES FOR PASSPORTS

SOCIAL NETWORKERS LOOKING TO EXPAND THEIR FRIENDS LIST PROFESSIONALS LEARNING ABOUT

COLLEAGUES AT CONFERENCES

RECONNECTING OUT-‐OF-‐TOUCH FAMILY MEMBERS

ONLINE SHOPPERS VERIFYING ONLINE SELLERS

INVESTIGATIVE JOURNALISTS RUNNING DOWN LEADS

SALES PROFESSIONALS LOOKING FOR NEW PROSPECTS

NETWORKERS SEEKING BUSINESS OPPORTUNITIES

LAW ENFORCEMENT

NON-‐PROFIT ORGANIZATIONS LOOKING FOR SUPPORTERS

FIANCÉS AND THEIR CURIOUS FAMILY MEMBERS

SOCIAL WORKERS WHO NEED TO KNOW MORE ABOUT THEIR CLIENTS

CALLER ID OF HARASSING PHONE CALLS

ALUMNI GROUPS ARRANGING REUNIONS

ADOPTED KIDS SEEKING THEIR BIOLOGICAL PARENTS

AIRLINES TRYING TO RETURN LOST LUGGAGE

CHECKING OUT A PROSPECTIVE SOCIAL NETWORK CONNECTION

CHECKING OUT A PROSPECTIVE DATE

CHECKING OUT A PROSPECTIVE TENANT

FINDING PEOPLE THAT HAVE THE SAME ILLNESS AS YOU

RESEARCHING A PROSPECTIVE EMPLOYEE

ANYONE RETRIEVING COURT RECORDS

REGULATED

Lots of uses for your data … some regulated.

LAWYERS NEEDING QUICK ACCESS TO COURT RECORDS

BANKING SERVICES

RESEARCH

SHARING

LEARNING ABOUT A BUSINESS

Confession

Opt-‐out doesn’t always mean deletion. Confession

Jane Hampton (309)555-8931

Jane Hampton 06/23/1998 123 Main Peoria, IL

Jane Hampton 123 Main Peoria, IL [email protected]

Listen to your toughest critics. 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

Lesson

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

Regulation

When towns were small, personal anonymity was low …

“The only thing worse than being talked about, is not being talked about.”

− Oscar Wilde

Lesson

“Good Fences Make Good Neighbors”

− Robert Frost

Urban populations grew along with personal anonymity…

Lesson

0

20

40

60

80

100

120

1850 1890 1930 1970 2010

Pri

vacy

Exp

ecta

tion

s !

… we’re suffering from Privacy Vertigo. Confession

“Rockwell” Era

Urban Density

“Good Fences” Era “Privacy Ver.go”

Era

Online Density

Peer to Peer Corpora.on & Customer/Employee

Government & Ci.zen

Your God & You

Privacy Rights !

Power Disparity !

In privacy contexts, Power matters. Lesson

How to unpack Privacy? Think PPP.

PERILS

Lesson

Mapping Places-‐Players-‐Perils Cases M

ORE P

LAYER P

OW

ER G

AP

Peers

Parents

Employers/Landlords/Insurers

Governments

MO R E P R I V A T E P L A C E S

Priv

ate

Curt

ilage

Publ

ic

Lesson


MORE P

LAYER P

OW

ER G

AP

News of the World phone hacking

Rutgers student commits suicide aXer spied by

webcam

FBI GPS criminal surveillance Google privacy

policy unifica?on

Target finds out teen pregnant before parents

GM OnStar tracks users

Woman caught naked by Google

Street View

Actress sues IMDB over revealing her

age

US deports Bri?sh tourists over Tweets

FB user sets fire to home aXer de-‐

friending

"Girls Around Me" pulled from market

Health orgs use Twi[er to track

illness

NSA internet ci?zen surveillance

Georgia teacher fired aXer pos?ng

vaca?on pics

Places-‐Players-‐Perils Cases

Ethically Challenging

Lesson


MORE P

LAYER P

OW

ER G

AP

News of the World phone hacking

Rutgers student commits suicide aXer spied by

webcam

FBI GPS criminal surveillance Google privacy

policy unifica?on

Target finds out teen pregnant before parents

GM OnStar tracks users

Woman caught naked by Google

Street View

Actress sues IMDB over revealing her

age

US deports Bri?sh tourists over Tweets

FB user sets fire to home aXer de-‐

friending

"Girls Around Me" pulled from market

Health orgs use Twi[er to track

illness

NSA internet ci?zen

surveillance Georgia teacher fired aXer pos?ng

vaca?on pics

Big brother is watching (duh).

“We’re being asked to trust without being able to verify.” − Alex Howard (big data journalist)

Pres. Obama calls for more transparency in FISA court and surveillance laws

NSA chief announces plan to replace 1,000 sysadmins with machines

Confession

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

Lesson

Wisdom

Knowledge

Informa.on

Data

A head in the clouds < 20 years Prediction

$27,100

$13,500

$6,800

$3,400

$1,700

$850

$420

$210

$100

$53

$26

$13

$7

$3 $2

$1 $1

$10

$100

$1,000

$10,000

$100,000

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042

Cost per Month (000s)

Year

Less than $700K per year

More than $325M per year Human Brain •  20,000 TFlops •  2,500 Terabytes

Chris Westbury, University of Alberta

à 4 M AWS m1.large nodes

“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

“… the essen.al crime that contained all others in itself. Thoughtcrime, they called it.”

– George Orwell

Big data inferences are not thoughtcrimes. Lesson

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

Confession

“To Serve Man” is a cookbook. Confession

“If you’re not paying for the product, you are the product.”

− Claire Wolfe (paraphrased)

Sometimes you’re in a public place when you think you’re in a private place.

“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.

Confession

John Foreman’s Excellent Disney Adventure

Confession

Felon Classi\ier

Objec.ve If someone has minor offenses on their criminal record, do they also have felonies?

Model Learner 250 M Defendants Feature

Extrac.on

15K Labels

15K Predictors Cleaning

Linking

Sampling

Bloomberg ar.cle: hDp://bloom.bg/1eMtnug

How does the Felon Classi\ier work?

Gender Eye Color Ta[oos Criminal Offenses Score

Over Threshold of 3.5?

Likely Felon?

NO

YES

Hazel (+1.7)

Male Blue 2 + (+1.3) Traffic only

Female (-‐0.5) Brown < 2 4 or fewer misdemeanors (+1.9)

YES

Green 8 or fewer misdemeanors

NO 4.4

Hazel

Male (+0.1) Blue 2 + Traffic only (-‐0.5)

Female Brown (+1.2) < 2 (+0.1) 4 or fewer misdemeanors

YES

Green 8 or fewer misdemeanors

NO 0.9

Bloomberg ar.cle: hDp://bloom.bg/1eMtnug Blog widget: hDp://jimadler.me

Classi\iers depend on policy as much as technology.

AN

ARCH

Y

T Y R A N N Y

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

0.0% 5.0% 10.0% 15.0% 20.0%

False Negative Rate

False Positive Rate

Threshold: 0.66 FP Rate: 5% FN Rate: 22%

Threshold: 1.1 FP Rate: 1% FN Rate: 40%

Threshold: -‐1.82 FP Rate: 19% FN Rate: 0%

Confession

NYC Stop & Frisk Found Unconstitutional Ruling

“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

Minorities 50%

90% of Criminals are Minorities

Criminals

“Half the money I spend on advertising is wasted; the trouble is I don't know which half.”

Lesson

PMinority is a Criminal =PCriminal is a Minority

PMinority

PCriminal

PMinority is a Criminal =90%50%

5%= 9%

PMinority is NOT a Criminal =100−PMinority is a Criminal

= 91%

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?

All NYC Residents

Minorities 50%

Criminals 5%

90% of Criminals

are Minorities

10% of Criminals are Not

Minorities

Bayes’ Rule

Asians 10%

When might Stop & Frisk be OK? Lesson


PMinority

PCriminal


5%= 45%


= 55%

All NYC Residents

Criminals 5%

Bayes’ Rule

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?

What about Newark’s Stop & Frisk? Lesson


PMinority

PCriminal


20%= 36%


= 64%

All Newark Residents

Bayes’ Rule

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?

Minorities 50%

Criminals 20%

hDp://www.ny.mes.com/2014/02/25/nyregion/newark-‐stop-‐and-‐frisk-‐data-‐is-‐analyzed.html

Hilary Mason’s Maxim

Math + Code = Awesome

Quants

Making a killing on Wall Street but s.ll can’t impress the chicks

Weakonomics.com

Lesson

Corollary to Mason’s Maxim

Values * (Math + Code) = Awesome

Lesson

“The beatings will continue until morale improves.”

Prediction

Unthinkable

Radical

Acceptable

Sensible

Popular

Policy Facebook Newsfeed

Ad Targeting

Facebook Beacon

Pre-crime

Skynet

Overton Window

Living within a Filter Bubble (with apologies to Eli Pariser)

Lesson

… but then we reshape our tools …

“We shape our tools …

… and thereawer our tools shape us.”

− Marshall McLuhan

Lesson “No one here gets out alive.” − Jim Morrison

Adapt, Invent

Scru.nize, Incen.vize

Listen, Learn Geeks

Wonks Suits

Questions?

Jim Adler www.metanau.x.com [email protected] @jim_adler

Confessions of a “Recovering” Data Broker: Responsible Innovation in the Age of Big Data, Big...

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Transcript of Confessions of a “Recovering” Data Broker: Responsible Innovation in the Age of Big Data, Big...