everyone so this other sort of wave a
question and that's the question what
does Minority Report Black Mirror a 1984
all have in common
now it's not the fact that they're all
forms of media you know books films TV
shows nor is it the fact that are about
dystopian futures but in says the fact
that they each talked about predicting
crime in one form or another whether
that's the precogs in minority reports
the Ricola
in black mirror or the for police in
1984 each of these forms of media look
at how we could predict crime but more
specifically the repercussions of doing
so and that's what I'm going to be
talking about today
and we talk about how we can use natural
language processing to predict crime but
for those of you that know me well know
I'm not a mathematician and also not a
police officer so why am I talking about
natural language processing which is
quite mathy and predictive policing
which has the name may suggest it's all
to do with law with crime what it comes
down to this quote the idea that
intrusion analysis security analysis
it's about far more than the tools we
use it's about everything and looking at
new ways that we can protect ourselves
from attacks but also predict those
attacks in the first place so what am I
actually going to be talking about today
well I want to break it up into three
main areas I don't talk about what
predictive policing actually is I want
to talk about what natural language
processing is and then finally I wanna
talk about how we can merge these two
ideas together how we can predict crime
using natural language processing
provide you know
my name is James Stevenson and this time
two years ago now I was a student at the
University of South Wales spitting
convenience security before that I was
an intern at alert logic a cloud
security company and these days I'm a
software engineer at BT security
Praetorians ready alert what is
predictive policing I keep thinking
about it what would actually isn't
because if we're going to use natural
language processing to predict crime but
we kind of need to know what predictive
policing it's
and it comes down to two main areas
location-based predictive policing and
individual based predictiveness now
location-based predictive policing as
the name suggests is all about looking
at an area so saying in this area in the
future is a crime likely took it now
this map is a great example of
location-based predictive policing so
this is a map of London between a
specific time period or the darker the
color in terms that the more crime has a
kid now this is a great example because
we can say okay if a crime has occurred
under these specific circumstances in
the past but it means that crime isn't
likely to occur under these same
circumstances in the future
now today were actually gonna be
focusing on individual-based predictive
policing now individual based predictive
policing is all about looking at an
individual and saying how likely is this
individual to Connecticut and when it
comes to this type of predictive
policing with masks different questions
will go down different routes different
avenues I will be left with that school
no today we're going to focus on three
types or three approaches that can allow
us to do justice the first of these
approaches we're gonna look at is called
strain theory now strain theory is the
idea that society puts pressure on
individuals to achieve specific thoughts
like the American dream but when
individuals lack the means to achieve
those goals the more likely técnicos so
that they can achieve them the next
theory that we're gonna look at is
called social control here now social
control theory is the idea that people
who lack close relationships commitments
values on roles are more likely to
commit crimes because they don't have
those relationships or values as an
anchor in society and the final theory
that we're going to look at today it's
called social disorganization view now
what this very states is it says that
location is key if you live for a work
in an area known for a specific type of
crime this theory states an intrinsic
link by being that the more likely
definitely
so so far we've looked at what
predictive policing is different types
of predictive policing and how we can
use predictive policing approaches to
predict call but this talk is all about
natural language process so about how we
can use natural language processing to
do just that but before we talk about
natural language processing we need to
talk about language this serve us as
human beings language comes down to
these three main theories speaking
reading and writing things that we all
do every day so because we do these
things every day most of us or maybe
some of us will be able to answer this
question Harris - grunts plus England
equals what now the answer isn't London
because her sister France has London is
- ain't it now if we didn't know that
that was the answer why did we know that
was it well we would have known that
that was the answer because of the
experiences of head we've read books
go on the internet spoken to people and
that's all built on knowledge base and
our understanding so then if we were to
give that question to our natural
language processing machine would it be
able to answer it well yes but only if
we gave it the right context so this is
the Wikipedia article for London and if
we fed this into our natural language
processing machine you would learn from
that surrounding context northern that
London is a city the only in that London
is in the UK of which England is as well
building that knowledge base and
building down so that's how natural
language processing works had a
sentiment analysis week because
sentiment analysis is all about that
tool looking at a specific piece of text
and saying what is the emotion what is
the sentiment behind that text however
it comes to us as human beings we have
eight main pillars to our emotions but
the sentiment analysis really really
care about
- that's positive emotions and negative
emotions so how do we translate those
eight
hell is down to two well well we're
talking about positive emotions we're
really talking about trust joy anger
this price and given the red herring and
what we're talking about negative
emotions we're talking about disgust
sadness fear and anticipation and so if
those are the emotions that we're
talking about when we referred to
sentiment analysis how do we actually
get that emotion from text well if the
same as in most machine learning
approaches we take a massive dataset now
that data set for us it's going to be
fresh drawn abuse each element in that
set is broken down into two sub sections
the actual review and then the sentiment
of that review for example I love my
local pizza restaurant positive sentence
well this place has really gone downhill
negative sentence we then break this
dataset down into we have our training
set and we have our testing set when it
comes to training our natural language
processing machine we ask it to go
through those entities and to have a
look at what keywords are more prominent
with a positive sentiment and what
keywords are more prominent be negative
certain then when it comes to testing we
ask it to go through the remaining
entities and fit for it to tell us what
the sentiment is and then if that
matches the sentiment we know them to
have cooked so if it doesn't it mean
something as well more so that's how
natural language processing works
that's have sentiment analysis works
what are it exists what are some
examples of natural language processing
in the real well this is a WS comprehend
or specifically comprehending medical
which is a delicious approach to natural
language processing when it comes to
medicine and healthcare a doctor or
health care professional will type in a
patient's details symptoms information
the natural language processing tool
will go off do its thing and it will
come back with T bits of information the
things that have that health care
professional Austin it next we have
Taylor a I know Taylor I was Microsoft's
approach to natural language processing
when it came to a Twitter chat it
Tarek's response to people depending on
how people smoked it now it's quite
controversial it lasted just under 24
hours but nonetheless is a great example
and then finally we have predictive text
so overnight you had an Android or an
iPhone where that predictive text this
probably works in your device by using
natural language process so there we
have three great examples so healthcare
communications and mobile phones but
none of those examples look at how we
can use natural language processing to
predict decline which is why we're here
for this talk so let's do that
well this is Alice and it's Alice's job
to do just that it's Alice's job to
predict crime where she currently does
this she individually and manually goes
to different web sites chat forums
social media accounts and two profiles
individuals on their likelihood of
commit crime but that's slow and
laborious so how can we take this to the
next level well we can automate it we
can scrape these websites the same
information that would help we can use
natural language processing on the
response and then we can return to Alice
a school a score of how likely that
individual is to commit a crime and this
is what we're gonna be focusing on for
the rest of the day we're going to be
talking about how we could build a
conceptual framework that allows us to
do just this so if we were to create
this framework what would we need to do
well first of all Alice we need to sit
down and decide on the impact for we'll
be profiling and what is the impact of
those individuals if that individual was
to commit that crime or was to perform
that attacked what would the impact be
and this comes down for those three main
areas the loss of confidentiality
integrity and available once we have
that impact we will work out our
likelihood what is the likelihood of
that individual committing that crime or
performing that attack well this is
where we're going to go back to those
predictive policing approaches that we
mentioned earlier on first of all you
scrape these websites for that text we
use natural language processing on the
response and now we're saying
does that text continue reference to any
goals or aspirations and then if it does
what is the sentiment next we take that
same bit of text we see now does that
text contain reference to any close
relationships any individuals any groups
in the organizations and if so what is
the sentiment and then finally we take
that same bit of text once again and we
say now does that text contain reference
to the individuals location if so is
that the location no that type of crime
and then finally what is the sentiment
we think of through each of these trees
aggregating a score as we go and that
score is our overall like it we can then
use that whoever our impact to get our
risk and that risk is the risk that this
individual poses to Alice entity now
that we have that risk it's just about
collecting too much information as we
can using natural language processing
moon collect information like come on
topics trans age gender and race
occupation salary and religion and then
any dates and times now the reason why
we haven't focused on this information
today it's because this information has
the scope and the potential of becoming
significantly more bucks and that's
really a talk for another day so finally
as part of this conceptual framework
it's about creating a naming convention
an image invention that we can instantly
clean information from without including
any of that personally identifiable
information so this name is broken down
into four main areas the source of the
data the time it occurred reverse score
and then a pseudo-random word that gives
the name some uniqueness so we looked at
what predictive policing is we look at
what natural language processing is we
also look at how we can use these two
ideas together to predict crime but you
might be thinking well James that's
great but why are we talking about
predictive policing and a computer
security conference and well it comes
back to this quote the idea that
intrusion analysis
security analysis it's about far more
than the tools we use it's about in
evicting but it's also about thinking
outside of the box and looking at new
ways that we can protect ourselves from
attacks but also predict those attacks
in the first place so already knows I'm
gonna go through some questions that I
only get that's one of this book
I'll close the talk off and then if we
have any additional questions I'll give
written them so the first question we
have here is is predictive policing
better than what is now I think the
answer here is no predictive policing is
a tool it's a supplement it's something
that should be used in addition to
normal placing and isn't there to
replace police nor is it that to replace
police intuition the next question we
have is predictive policing bias again
the short answer is yes
but even policing is quite bias the
prison for that is predictive policing
is garbage in garbage out if our data is
bias it means how frameworks are going
to be biased also and the problem with
crime data is that it's intrinsically
bias because we have crimes that aren't
documented maybe like assault means that
the data we get from that isn't accurate
is it representative of the real world
and that's fine as long as we remember
the answer to that first question as
long as we remember that predictive
policing is a tool it's a suite and it's
to be used in addition to what we're
here to do next question there is is
predictive policing used in the real
world so pretty deep listens used in the
UK it's also used in the u.s. one
example that received quite a lot of
media attention was the LAPD in the
States
they had a scheme called laser the way
that laser which is assigned a score to
ex-offenders
that school was based on their behavior
and the way that that worked is anyone
in that top bracket would then receive a
follow-up visit crumples the penultimate
question we have here is how good is
natural language processing at picking
up differences or nuances in text and
there's an example I like living for
this and it was an example of a natural
language processing tool that was
created to simultaneously understand two
different languages
I think that's really interesting
because that goes to share than actually
in some cases natural language
processing can be better at
understanding text than some of us as
human beings and finally what's next
well specifically for this framework is
about creating a proof of concept a
prove a concept that allows us to do
just this allows us to look at risk
levels for individuals that allows us to
pivot off social media information and
allows it to a power ottoman and allows
us to look at crime signals so
indicators the cry however that is this
talk came to a close if you do have any
questions feel free to ask me now come
find me afterwards or i'm also on
twitter and underscore james deals
thanks