Whitney Merrill: Predicting Crime in a Big Data World

32c3 preroll music

Angel: I introduce Whitney Merrill.
She is an attorney in the US

and she just recently, actually
last week, graduated

to her CS masters in Illinois.

applause

Angel: Without further ado:
‘Predicting Crime In A Big Data World’.

cautious applause

Whitney Merrill: Hi everyone.
Thank you so much for coming.

I know it´s been a exhausting Congress,
so I appreciate you guys coming

to hear me talk about Big
Data and Crime Prediction.

This is kind of a hobby of mine, I,

in my last semester at Illinois,
decided to poke around

what´s currently happening, how these
algorithms are being used and kind of

figure out what kind of information can be
gathered. So, I have about 30 minutes

with you guys. I´m gonna do a broad
overview of the types of programs.

I´m gonna talk about what Predictive
Policing is, the data used,

similar systems in other areas
where predictive algorithms are

trying to better society,
current uses in policing.

I´m gonna talk a little bit about their
effectiveness and then give you

some final thoughts. So, imagine,

in the very near future a Police
officer is walking down the street

wearing a camera on her collar.
In her ear is a feed of information

about the people and cars she passes
alerting her to individuals and cars

that might fit a particular crime
or profile for a criminal.

Early in the day she examined a
map highlighting hotspots for crime.

In the area she´s been set to patrol
the predictive policing software

indicates that there is an 82%
chance of burglary at 2 pm,

and it´s currently 2:10 pm.
As she passes one individual

her camera captures the
individual´s face, runs it through

a coordinated Police database - all of the
Police departments that use this database

are sharing information. Facial
recognition software indicates that

the person is Bobby Burglar who was
previously convicted of burglary,

was recently released and is now currently
on patrole. The voice in her ear whispers:

50 percent likely to commit a crime.
Can she stop and search him?

Should she chat him up?
Should see how he acts?

Does she need additional information
to stop and detain him?

And does it matter that he´s
carrying a large duffle bag?

Did the algorithm take this into account
or did it just look at his face?

What information was being
collected at the time the algorithm

chose to say 50% to provide
the final analysis?

So, another thought I´m gonna
have you guys think about as I go

through this presentation, is this
quote that is more favorable

towards Police algorithms, which is:
“As people become data plots

and probability scores, law enforcement
officials and politicians alike

can point and say: ‘Technology is void of
the racist, profiling bias of humans.’”

Is that true? Well, they probably
will point and say that,

but is it actually void of
racist, profiling humans?

And I´m gonna talk about that as well.

So, Predictive Policing explained.
Who and what?

First of all, Predictive Policing
actually isn´t new. All we´re doing

is adding technology, doing better,
faster aggregation of data.

Analysts in Police departments have been
doing this by hand for decades.

These techniques are used to create
profiles that accurately match

likely offenders with specific past
crimes. So, there´s individual targeting

and then we have location-based
targeting. The location-based,

the goal is to help Police
forces deploy their resources

in a correct manner, in an efficient
manner. They can be as simple

as recommending that general crime
may happen in a particular area,

or specifically, what type of crime will
happen in a one-block-radius.

They take into account the time
of day, the recent data collected

and when in the year it´s happening
as well as weather etc.

So, another really quick thing worth
going over, cause not everyone

is familiar with machine learning.
This is a very basic breakdown

of training an algorithm on a data set.

You collect it from many different
sources, you put it all together,

you clean it up, you split it into 3 sets:
a training set, a validation set

and a test set. The training set is
what is going to develop the rules

in which it´s going to kind of
determine the final outcome.

You´re gonna use a validation
set to optimize it and finally

apply this to establish
a confidence level.

There you´ll set a support level where
you say you need a certain amount of data

to determine whether or not the
algorithm has enough information

to kind of make a prediction.
So, rules with a low support level

are less likely to be statistically
significant and the confidence level

in the end is basically if there´s
an 85% confidence level

that means there´s an 85% chance that the
suspect, e.g. meeting the rule in question,

is engaged in criminal conduct.
So, what does this mean? Well,

it encourages collection and hoarding
of data about crimes and individuals.

Because you want as much information
as possible so that you detect

even the less likely scenarios.

Information sharing is also
encouraged because it´s easier,

it´s done by third parties, or even
what are called fourth parties

and shared amongst departments.
And here, your criminal data again

was being done by analysts in Police
departments for decades, but

the information sharing and the amount
of information they could aggregate

was just significantly more difficult. So,

what are these Predictive Policing
algorithms and software…

what are they doing? Are they
determining guilt and innocence?

And, unlike a thoughtcrime, they
are not saying this person is guilty,

this person is innocent. It´s creating
a probability of whether or not

the person has likely committed
a crime or will likely commit a crime.

And it can only say something
to the future and the past.

This here is a picture from
one particular piece of software

provided by HunchLab; and patterns
emerge here from past crimes

that can profile criminal types and
associations, detect crime patterns etc.

Generally in this types of algorithms
they are using unsupervised data,

that means someone is not going through
and saying true-false, good-bad, good-bad.

There´s just 1) too much information and
2) they´re trying to do clustering,

determine the things that are similar.

So, really quickly, I´m also gonna
talk about the data that´s used.

There are several different types:
Personal characteristics,

demographic information, activities
of individuals, scientific data etc.

This comes from all sorts of sources,
one that really shocked me, was,

and I´ll talk about it a little bit in the
future, but, is the radiation detectors

on New York City Police are
constantly taking in data

and it´s so sensitive, it can detect if
you´ve had a recent medical treatment

that involves radiation. Facial
recognition and biometrics

are clear here and the third-party
doctrine – which basically says

in the United States that you have no
reasonable expectation of privacy in data

you share with third parties –
facilitates easy collection

for Police officers and Government
officials because they can go

and ask for the information
without any sort of warrant.

For a really great overview: a friend of
mine, Dia, did a talk here at CCC

on “The architecture of a street level
panopticon”. Does a really great overview

of how this type of data is collected
on the streets. Worth checking out

´cause I´m gonna gloss over
kind of the types of data.

There is in the United States what
they call Multistate Anti-Terrorism

Information Exchange Program which
uses everything from credit history,

your concealed weapons permits,
aircraft pilot licenses,

fishing licences etc. that´s searchable
and shared amongst Police departments

and Government officials and this is just
more information. So, if they can collect

it, they will aggregate it into a data
base. So, what are the current uses?

There are many, many different
companies currently

making software and marketing
it to Police departments.

All of them are slightly different, have
different features, but currently

it´s a competition to get clients,
Police departments etc.

The more Police departments you have
the more data sharing you can sell,

saying: “Oh, by enrolling you’ll now have
x,y and z Police departments’ data

to access” etc. These here
are Hitachi and HunchLab,

they both are hotspot targeting,
it´s not individual targeting,

those are a lot rarer. And it´s actually
being used in my home town,

which I´ll talk about in a little bit.
Here, the appropriate tactics

are automatically displayed for officers
when they´re entering mission areas.

So HunchLab will tell an officer:
“Hey, you´re entering an area

where there´s gonna be burglary that you
should keep an eye out, be aware”.

And this is updating in live time and
they´re hoping it mitigates crime.

Here are 2 other ones, the Domain
Awareness System was created

in New York City after 9/11
in conjunction with Microsoft.

New York City actually makes
money selling it to other cities

to use this. CCTV-cameras
are collected, they can…

If they say there´s a man
wearing a red shirt,

the software will look for people
wearing red shirts and at least

alert Police departments to
people that meet this description

walking in public in New York
City. The other one is by IBM

and there are quite a few, you know, it´s
just generally another hotspot targeting,

each have a few different features.
Worth mentioning, too, is the Heat List.

This targeted individuals. I’m from the
city of Chicago. I grew up in the city.

There are currently 420 names, when
this came out about a year ago,

of individuals who are 500 times more
likely than average to be involved

in violence. Individual names, passed
around to each Police officer in Chicago.

They consider the rap sheet,
disturbance calls, social network etc.

But one of the main things they considered
in placing mainly young black individuals

on this list were known acquaintances
and their arrest histories.

So if kids went to school or young
teenagers went to school

with several people in a gang – and that
individual may not even be involved

in a gang – they’re more likely to
appear on the list. The list has been

heavily criticized for being racist,
for not giving these children

or young individuals on the list
a chance to change their history

because it’s being decided for them.
They’re being told: “You are likely

to be a criminal, and we’re gonna
watch you”. Officers in Chicago

visited these individuals would do knock
and announce with a knock on the door

and say: “Hi, I’m here, like just
checking up what are you up to”.

Which you don’t need any special
suspicion to do. But it’s, you know,

kind of a harassment that
might cause a feedback,

back into the data collected.

This is PRECOBS. It’s currently
used here in Hamburg.

They actually went to Chicago and
visited the Chicago Police Department

to learn about Predictive Policing
tactics in Chicago to implement it

throughout Germany, Hamburg and Berlin.

It’s used to generally
forecast repeat-offenses.

Again, when training data sets you need
enough data points to predict crime.

So crimes that are less likely to
happen or happen very rarely:

much harder to predict. Crimes that
aren’t reported: much harder to predict.

So a lot of these software…
like pieces of software

rely on algorithms that are hoping
that there’s a same sort of picture,

that they can predict: where and when
and what type of crime will happen.

PRECOBS is actually a term with a plan

– the movie ‘Minority Report’, if you’re
familiar with it, it’s the 3 psychics

who predict crimes
before they happen.

So there’re other, similar systems
in the world that are being used

to predict whether or not
something will happen.

The first one is ‘Disease and Diagnosis’.
They found that algorithms are actually

more likely than doctors to predict
what disease an individual has.

It’s kind of shocking. The other is
‘Security Clearance’ in the US.

It allows access to classified documents.
There’s no automatic access in the US.

So every person who wants to see
some sort of secret cleared document

must go through this process.
And it’s vetting individuals.

So it’s an opt-in process. But here
they’re trying to predict who will

disclose information, who will
break the clearance system;

and predict there… Here, the error rate,
they’re probably much more comfortable

with a high error rate. Because they
have so many people competing

for a particular job, to get
clearance, that if they’re wrong,

that somebody probably won’t disclose
information, they don’t care,

they just rather eliminate
them than take the risk.

So I’m an attorney in the US. I have
this urge to talk about US law.

It also seems to impact a lot
of people internationally.

Here we’re talking about the targeting
of individuals, not hotspots.

So targeting of individuals is
not as widespread, currently.

However it’s happening in Chicago;

and other cities are considering
implementing programs and there are grants

right now to encourage
Police departments

to figure out target lists.

So in the US suspicion is based on
the totality of the circumstances.

That’s the whole picture. The Police
officer, the individual must look

at the whole picture of what’s happening
before they can detain an individual.

It’s supposed to be a balanced
assessment of relative weights, meaning

– you know – if you know that the
person is a pastor maybe then

pacing in front of a liquor
store, is not as suspicious

as somebody who’s been convicted
of 3 burglaries. It has to be ‘based

on specific and articulable facts’. And
the Police officers can use experience

and common sense to determine
whether or not their suspicion…

Large amounts of networked data generally
can provide individualized suspicion.

The principal components here… the
events leading up to the stop-and-search

– what is the person doing right before
they’re detained as well as the use

of historical facts known about that
individual, the crime, the area

in which it’s happening etc.
So it can rely on both things.

No court in the US has really put out
a percentage as what Probable Cause

and Reasonable Suspicion. So ‘Probable
Cause’ you need to get a warrant

to search and seize an individual.
‘Reasonable Suspicion’ is needed

to do stop-and-frisk in the US – stop
an individual and question them.

And this is a little bit different than
what they call ‘Consensual Encounters’,

where a Police officer goes up to you and
chats you up. ‘Reasonable Suspicion’

– you’re actually detained. But I had
a law professor who basically said:

“30%..45% seem like a really good number

just to show how low it really is”.You
don’t even need to be 50% sure

that somebody has committed a crime.

So, officers can draw from their own
experience to determine ‘Probable Cause’.

And the UK has a similar
‘Reasonable Suspicion’ standard

which depend on the circumstances
of each case. So,

I’m not as familiar with UK law but I
believe even that some of the analysis-run

‘Reasonable Suspicion’ is similar.

Is this like a black box?
So, I threw this slide in

for those who are interested
in comparing this US law.

Generally a dog sniff in the US
falls under a particular set

of legal history which is: a
dog can go up, sniff for dogs,

alert and that is completely okay.

And the Police officers can use that
data to detain and further search

an individual. So is an algorithm similar
to the dog which is kind of a black box?

Information goes out, it’s processed,
information comes out and

a prediction is made.
Police rely on the ‘Good Faith’

in ‘Totality of the Circumstances’
to make their decision. So there’s

really no… if they’re
relying on the algorithm

and think in that situation that
everything’s okay we might reach

a level of ‘Reasonable Suspicion’ where
the individual can now pat down

the person he’s decided on the street
or the algorithm has alerted to. So,

the big question is, you know, “Could the
officer consult predictive software apps

in any individual analysis. Could he
say: ‘60% likely to commit a crime’”.

In my hypo: Does that
mean that the person

without looking at anything
else detain that individual.

And the answer is “Probably not”. One:
predictive Policing algorithms just

can not take in the Totality of the
Circumstances. They have to be

frequently updated, there are
things that are happening that

the algorithm possibly could
not have taken into account.

The problem here is
that the algorithm itself,

the prediction itself becomes part
of Totality of the Circumstances,

which I’m going to talk
about a little bit more later.

But officers have to have Reasonable
Suspicion before the stop occurs.

Retroactive justification
is not sufficient. So,

the algorithm can’t just say:
“60% likely, you detain the individual

and then figure out why you’ve
detained the person”. It has to be

before the detention actually happens.
And the suspicion must relate

to current criminal activity. The
person must be doing something

to indicate criminal activity. Just
the fact that an algorithm says,

based on these facts: “60%”,
or even without articulating

why the algorithm has
chosen that, isn’t enough.

Maybe you can see a gun
shaped bulge in the pocket etc.

So, effectiveness… the
Totality of the Circumstances,

can the algorithms keep up?
Generally, probably not.

Missing data, not capable of
processing this data in real time.

There’s no idea… the
algorithm doesn’t know,

and the Police officer probably
doesn’t know the all of the facts.

So the Police officer can take
the algorithm into consideration

but the problem here is: Did the algorithm
know that the individual was active

in the community, or was a politician, or

that was a personal friend of the officer
etc. It can’t just be relied upon.

What if the algorithm did take into
account that the individual was a Pastor?

Now that information is counted twice
and the balancing for the Totality

of the Circumstances is off. Humans
here must be the final decider.

What are the problems?
Well, there’s bad underlying data,

there’s no transparency into
what kind of data is being used,

how it was collected, how old it
is, how often it’s been updated,

whether or not it’s been verified. There
could just be noise in the training data.

Honestly, the data is biased. It was
collected by individuals in the US;

generally there’ve been
several studies done that

black, young individuals are
stopped more often than whites.

And this is going to
cause a collection bias.

It’s gonna be drastically disproportionate
to the makeup of the population of cities;

and as more data has been collected on
minorities, refugees in poor neighborhoods

it’s gonna feed back in and of course only
have data on those groups and provide

feedback and say:
“More crime is likely to

happen because that’s where the data

was collected”. So, what’s
an acceptable error rate, well,

depends on the burden of proof. Harm
is different for an opt-in system.

You know, what’s my harm if I don’t
get clearance, or I don’t get the job;

but I’m opting in, I’m asking to
being considered for employment.

In the US, what’s an error? If you
search and find nothing, if you think

you have Reasonable Suspicion
based on good faith,

both on the algorithm and what
you witness, the US says that it’s

no 4th Amendment violation,
even if nothing has happened.

It’s very low error
false-positive rate here.

In Big Data, generally, and
machine-learning it’s great!

Like 1% error is fantastic! But that’s
pretty large for the number of individuals

stopped each day. Or who might
be subject to these algorithms.

Because even though there’re only
400 individuals on the list in Chicago

those individuals have been
listed basically as targets

by the Chicago Police Department.

Other problems include database errors.
Exclusion of evidence in the US

only happens when there’s gross
negligence or systematic misconduct.

That’s very difficult to prove, especially
when a lot of people view these algorithms

as a big box. Data goes in,
predictions come out, everyone’s happy.

You rely and trust on the
quality of IBM, HunchLab etc.

to provide good software.

Finally, some more concerns I have
include feedback loop auditing

and access to data and algorithms
and the prediction thresholds.

How certain must a prediction be
– before it’s reported to the Police –

that the person might commit a
crime. Or that crime might happen

in the individual area. If Reasonable
Suspicion is as low as 35%,

and reasonable Suspicion in the US has
been held at: That guy drives a car

that drug dealers like to drive,
and he’s in the DEA database

as a possible drug dealer. That was
enough to stop and search him.

So, are there Positives? Well, PredPol,

which is one of the services that
provides Predictive Policing software,

says: “Since these cities have
implemented there’s been dropping crime”.

In L.A. 13% reduction in
crime, in one division.

There was even one day where
they had no crime reported.

Santa Cruz – 25..29% reduction,
-9% in assaults etc.

One: these are Police departments
self-reporting these successes for…

you know, take it for what it is
and reiterated by the people

selling the software. But perhaps
it is actually reducing crime.

It’s kind of hard to tell because
there’s a feedback loop.

Do we know that crime is really being
reduced? Will it affect the data

that is collected in the future? It’s
really hard to know. Because

if you send the Police officers into
a community it’s more likely

that they’re going to affect that
community and that data collection.

Will more crimes happen because they
feel like the Police are harassing them?

It’s very likely and it’s a problem here.

So, some final thoughts. Predictive
Policing programs are not going anywhere.

They’re only in their wheelstart.

And I think that more analysis, more
transparency, more access to data

needs to happen around these algorithms.
There needs to be regulation.

Currently, a very successful way in which

these companies get data is they
buy from Third Party sources

and then sell it to Police departments. So
perhaps PredPol might get information

from Google, Facebook, Social Media
accounts; aggregate data themselves,

and then turn around and sell it to
Police departments or provide access

to Police departments. And generally, the
Courts are gonna have to begin to work out

how to handle this type of data.
There’s not case law,

at least in the US, that really knows
how to handle predictive algorithms

in determining what the analysis says.
And so there really needs to be

a lot more research and
thought put into this.

And one of the big things in order
for this to actually be useful:

if this is a tactic that had been used
by Police departments for decades,

we need to eliminate the bias in
the data sets. Because right now

all that it’s doing is facilitating and
continuing bias, set in the database.

And it’s incredibly difficult.
It’s data collected by humans.

And it causes initial selection bias.
Which is gonna have to stop

for it to be successful.

And perhaps these systems can cause
implicit bias or confirmation bias,

e.g. Police are going to believe
what they’ve been told.

So if a Police officer goes
on duty to an area

and an algorithm says: “You’re
70% likely to find a burglar

in this area”. Are they gonna find
a burglar because they’ve been told:

“You might find a burglar”?
And finally the US border.

There is no 4th Amendment
protection at the US border.

It’s an exception to the warrant
requirement. This means

no suspicion is needed to commit
a search. So this data is gonna go into

a way to examine you when
you cross the border.

And aggregate data can be used to
refuse you entry into the US etc.

And I think that’s pretty much it.
And so a few minutes for questions.

applause
Thank you!

Herald: Thanks a lot for your talk,
Whitney. We have about 4 minutes left

for questions. So please line up at
the microphones and remember to

make short and easy questions.

Microphone No.2, please.

Question: Just a comment: if I want
to run a crime organization, like,

I would target the PRECOBS
here in Hamburg, maybe.

So I can take the crime to the scenes

where the PRECOBS doesn’t suspect.

Whitney: Possibly. And I think this is
a big problem in getting availability

of data; in that there’s a good argument
for Police departments to say:

“We don’t want to tell you what
our tactics are for Policing,

because it might move crime”.

Herald: Do we have questions from
the internet? Yes, then please,

one question from the internet.

Signal Angel: Is there evidence that data
like the use of encrypted messaging

systems, encrypted emails, VPN, TOR,
with automated request to the ISP,

are used to obtain real names and
collected to contribute to the scoring?

Whitney: I’m not sure if that’s
being taken into account

by Predictive Policing algorithms,
or by the software being used.

I know that Police departments do
take those things into consideration.

And considering that in the US
Totality of the Circumstances is

how you evaluate suspicion. They are gonna
take all of those things into account

and they actually kind of
have to take into account.

Herald: Okay, microphone No.1, please.

Question: In your example you mentioned
disease tracking, e.g. Google Flu Trends

is a good example of preventive Predictive
Policing. Are there any examples

where – instead of increasing Policing
in the lives of communities –

where sociologists or social workers
are called to use predictive tools,

instead of more criminalization?

Whitney: I’m not aware if that’s…
if Police departments are sending

social workers instead of Police officers.
But that wouldn’t surprise me because

algorithms are being used to suspect child
abuse. And in the US they’re gonna send

a social worker in regard. So I would
not be surprised if that’s also being

considered. Since that’s
part of the resources.

Herald: OK, so if you have
a really short question, then

microphone No.2, please.
Last question.

Question: Okay, thank you for the
talk. This talk as well as few others

brought the thought in the debate
about the fine-tuning that is required

between false positives and
preventing crimes or terror.

Now, it’s a different situation
if the Policeman is predicting,

or a system is predicting somebody’s
stealing a paper from someone;

or someone is creating a terror attack.

And the justification to prevent it

under the expense of false positive
is different in these cases.

How do you make sure that the decision
or the fine-tuning is not going to be

deep down in the algorithm
and by the programmers,

but rather by the customer
– the Policemen or the authorities?

Whitney: I can imagine that Police
officers are using common sense in that,

and their knowledge about the situation
and even what they’re being told

by the algorithm. You hope
that they’re gonna take…

they probably are gonna take
terrorism to a different level

than a common burglary or
a stealing of a piece of paper

or a non-violent crime.
And that fine-tuning

is probably on a Police department

by Police department basis.

Herald: Thank you! This was Whitney
Merrill, give a warm round of applause, please!!

Whitney: Thank you!
applause

postroll music

Subtitles created by c3subtitles.de
in the year 2016. Join and help us!