using data for evil (2)

@fhr @duncan3ross @datakinduk

Before we start We aren’t actually evil*

We don’t want you to do these things**

But, if you catch yourself thinking that “this is a little bit like what we do” or “if we aren’t careful that could be us…” then maybe you ought to think carefully***

* mileage may vary

** other than for comedy purposes

*** and NOT do them. No, seriously

USING DATA FOR EVILFran Bennett,

CEO Mastodon C

@fhr

Duncan Ross,

Data and Analytics Director, Times Higher Education

@duncan3ross

2


The Categorical Imperative

“Act only according to that maxim whereby you can, at the same time, will that it should become a universal law”

He said that humans are ends-in-themselves, not means to an end

TOO PIOUS?

DAMN RIGHT!

http://en.wikipedia.org/wiki/Maxim_(philosophy)


Your quest for Global Data Dominance begins here

Doing evil doesn’t need to be about the big things

- We all have the ability to make the world a little worse

- Where would Khan have got to without his minions?

So, in 2014, how did people manage to do so much evil with data?


Our hypothesis For a data scientist

- Doing good deliberately is hard and Doing evil deliberately is hard

- Doing evil accidentally is easy

Hypothesis:

- Every data scientist has the capability to do good by thinking about what they do

Null hypothesis:

- Every data scientist has the capability to do good by not thinking about what they do

Source: xkcd


The problem

Too many people have been thinking

Things have got worse (good) not better (bad)


Step 1

Choose the right problem…


Quiz: which of these is most exciting?

① Analysing call ‘meta data’ to find out when people are entering (and leaving) a relationship

② Finding out how different people react to different drugs using medical data?

③ Predicting and changing behaviours through examining purchasing behaviour


Don’t care about impact! Analysis is cool, and powerful

Your analysis can have really great effect on people

Effects aren’t always as predicted

- Never measure the results

- Don’t worry about what might happen


Step 2

Data Misuse


Visualisation is evil


A bad visualisation is worth a thousand swear words

The (possibly ex) PM on Facebook

Facebook is EVIL

Terrorists are EVIL

Surely we can do something with this?


What could possibly go wrong? Let’s not confuse politicians with statistics

- Yes, the Royal Statistical Society is trying…

Gloss over issues like Bayes and Type I and Type II errors

Type II error Don’t intercept terrorist

Type I error Send police after ‘innocent’ victims


Facebook: the maths P(bad guy | +) = ??

P(+ | bad guy) = 0.999

P(bad guy) = 100/60,000,000

P(+ | good guy) = 0.001

Then:

P(bad guy | +) = 99.9/60,000 = 0.17%

https://duncan3ross.wordpress.com/2014/11/26/why-david-cameron-is-wrong-the-maths/


Take advantage when intuition is systematically wrong

http://duckofminerva.com/2013/07/bayes-stereotypin-and-rare-events.html


But wait a minute! If you have independent priors (I think Fran is a terrorist…) then won’t this make

Facebook’s predictions much, much better?

Yes!

So we should

- Give Facebook all of MI5’s data

- Just have mass surveillance of Facebook by MI5…


Step 3

Personal data


3 Evil Personal Data Options1. Know your enemy (by which we mean everyone)

2. Change their behaviour (make more money!)

3. If you can’t do 1 or 2, sell the data


But doesn’t anonymisation protect personal data?

There is nothing better than revealing people’s dirty secrets

Taxi & Limo Commission released every 2013 NYC taxi ride (detailed locations, timings, fares)

What could possibly go wrong?

http://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset/


Celebrity stalking! Good news! It’s really hard to guarantee anonymity. The more data you link

together, the easier it is to work backwards


Home addresses of strip club visitors! Find the residential addresses

where cabs get to after leaving dodgy locations between midnight and 6am

Filter down to specific individuals using property records

Find where else those people go to in cabs (maybe to where they work?)

Go big or go home

Knowing your enemy


Budge over, Nudge Unit

Change behaviour

Casinos:

Great place for plotting, or full ofsecret agents? We ask the important questions…


Data is everywhere: use it

Image: Casino Enterprise Management - Where’s the Money? Part 5: Gaming Density and Yielding the Floor

From a reputable source


Revenue vs human issues? Go for the revenue

The Necromantic Quadrant

And the Hell Cycle


Hell Cycle

Kickstarter Peak of Hysteria

Pit of Despair

Appears on Dave

Slope of Acceptanc

e

M25

IoT

Masssurveillance

Spark BadVisualisation

Big Table

IBMWatson

Pre-CrimeProfiling

AppleWatch

Deanonymisation

care.data

Google. Just Google


Where can you do most evil? In an organisation committed

to evil? In an organisation committed

to good? In an organisation committed

to shareholder value? In an organisation that isn’t

sure?

Image: mattbuck


Spectrum of evil

NOT EVIL ENOUGH


Necromantic Quadrant ™ 2013

SMERSH

NSA

E.ON

RSPCA

OWCAUNITSPECTRE COTT

EVIL GOOD

EFFEC

TIV

EN

ESS


Necromantic Quadrant ™ 2015

SMERSH

NSA

RSPCA

SPECTRE

EVIL GOOD

EFFEC

TIV

EN

ESS

Devils Angels

Self-regulatorsChallengers

Royal MailCAB

Wonga

Uber

Reminder: Three goals

Misuse Data

Get personal

Choose the right problem

BUT IF YOU DON’T WANT TO BE EVIL?

36 @duncan3ross @DataKindUK

• DataKind UK is a charity that believes we can make the world better by using data

• We work by linking data volunteers (you) with charities

COME AND JOIN DATAKIND


WHO HAVE WE WORKED WITH?

Children

Education

Health

Young people

Advice and support

International and community


DATAKIND UK TODAY

£

808 2

£850K

6,850

25 6


We are hiring!

London DataDive

17-19 July

Volunteers wanted

Join us: http://www.meetup.com/DataKind-UK/

THANK YOU

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