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Seems to me much will depend on the beliefs and attitudes of the
authorities as opposed to the experts. Looking for suspicious
patterns is traditional in policing and this is just the same --
except powered by a turbo-charged mechanism. If the cops start to
assume that the associations the software predicts are pretty well
infallible, there will be no escape for the innocent and therefore
this will be Minority Report (1984, The First Circle, etc.)<br>
<br>
Fascinating thread (nearly missed because of a most misleading
subject line)<br>
<br>
Cheers -- Mike<br>
<br>
<br>
<div class="moz-cite-prefix">On 23/04/2014 09:44, M.E.Sciubba wrote:<br>
</div>
<blockquote
cite="mid:CACTqU0HUdg6_jic111Nhi=yQkzaFK+fUB6RevA-58MC5Zghyxw@mail.gmail.com"
type="cite">
<div dir="ltr">Beyond the legal aspect about the prediction of
human actions and hence loss of basic civil rights, I think the
key point here is the "cultural" variation (if any) of the
values attached to what somebody tweets/sends through social
media. It is a sociological analysis of the interaction between
people(s) lives and (possible) threatening activities that is
lacking, if I got it right...
<div>
<br>
</div>
<div>Very interesting subject indeed, if I could only apply as a
telecommuter post-doc ;)</div>
<div><br>
</div>
<div>Cheers,</div>
<div><br>
</div>
<div>Eleonora</div>
<div><br>
</div>
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<br>
<div class="gmail_quote">2014-04-23 5:57 GMT+02:00 Alexander Yeh
<span dir="ltr"><<a moz-do-not-send="true"
href="mailto:asy@mitre.org" target="_blank">asy@mitre.org</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="">Zoltan Boka wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
Predictions are only as good as the initial data they're
based on. In<br>
this case that data could be incomplete or limited or
biased (for<br>
instance, lets say that one data point is the number of
arrests made on<br>
street x: lets also say that its police policy to harass
and arrest<br>
people living on street x- you can see how this can
infect the process.)<br>
<br>
But even if it can be done without bias and with a high
accuracy, the<br>
question remains- are some people literally destined to
commit crime and<br>
if so can that destiny be altered short of preemptively
arresting them?<br>
</blockquote>
<br>
</div>
If you have a guess at a who is likely, you could
concentrate undercover police to where (and when) that
person tends to go and also to follow that person around.
With enough evidence, you could also get search warrants and
warrants to intercept their phone, etc.<br>
<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="">
<br>
Sent from my iPhone<br>
<br>
On Apr 22, 2014, at 14:39, Adam Kilgarriff <<a
moz-do-not-send="true"
href="mailto:adam@lexmasterclass.com" target="_blank">adam@lexmasterclass.com</a><br>
</div>
<mailto:<a moz-do-not-send="true"
href="mailto:adam@lexmasterclass.com" target="_blank">adam@lexmasterclass.com</a>>>
wrote:<br>
<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="">
If a clever system can predict who is going to predict
a crime - with<br>
good, but far from 100%, accuracy, is the use it<br>
a) rational policing practice<br>
b) discriminatory<br>
to use that information?<br>
<br>
Seems to me, it's both.<br>
<br>
Marek says<br>
> But there are definitely many ways to abuse this
technology as well.<br>
<br>
I don't feel abuse is the main issue. Any use of it
is<br>
discriminatory. Should we trade off? Tough question.<br>
<br>
Adam<br>
<br>
<br>
On 22 April 2014 11:34, Marek Rei <<a
moz-do-not-send="true"
href="mailto:marek.rei@gmail.com" target="_blank">marek.rei@gmail.com</a><br>
</div>
<div class="">
<mailto:<a moz-do-not-send="true"
href="mailto:marek.rei@gmail.com" target="_blank">marek.rei@gmail.com</a>>>
wrote:<br>
<br>
Here's an interesting article about how Chicago
police is already<br>
applying such technology (in somewhat troubling
ways):<br>
<br>
<a moz-do-not-send="true"
href="http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist"
target="_blank">http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist</a><br>
<br>
I wouldn't say crime prediction technology by
itself is evil, it's<br>
more a question of how it's used. For example, I
wouldn't have a<br>
problem with a system that can prioritise a large
list of likely<br>
suspects after a crime has been committed, or is
able to flag a<br>
social media message calling for a hate crime. But
there are<br>
definitely many ways to abuse this technology as
well.<br>
<br>
Marek<br>
<br>
<br>
<br>
On Tue, Apr 22, 2014 at 10:55 AM, Christian
Pietsch<br>
</div>
<<a moz-do-not-send="true"
href="mailto:chr.pietsch@googlemail.com"
target="_blank">chr.pietsch@googlemail.com</a>
<mailto:<a moz-do-not-send="true"
href="mailto:chr.pietsch@googlemail.com"
target="_blank">chr.pietsch@googlemail.com</a>>>
<div>
<div class="h5"><br>
wrote:<br>
<br>
Hi Matthew,<br>
<br>
so you want to build a heuristic precrime
detector based on<br>
routine<br>
activities observed on social networks. Does
that mean that<br>
if, say, I<br>
tend to update my status at the same time as
some terrorist in<br>
your<br>
training set, your software will label me as
a likely<br>
terrorist and<br>
put me on a no-fly list? Will I get a chance
to prove my<br>
innocence?<br>
<br>
When you have some spare time, try to watch
Minority Report.<br>
Or did<br>
this movie inspire your project? Then you
must have<br>
misunderstood its<br>
message.<br>
<br>
Your suspect<br>
Christian<br>
<br>
<br>
On Mon, Apr 21, 2014 at 11:34:11AM -0400,
Matthew Gerber wrote:<br>
> Hello,<br>
><br>
> A new research position has opened
within our lab, and we<br>
are seeking M.S.,<br>
> Ph.D., and post-doc researchers.<br>
><br>
> One-sentence summary: We are mining
social media for<br>
indicators of<br>
> individual routine activities for the
purpose of improved<br>
crime prediction.<br>
><br>
> Longer summary: This project focuses on
the spatiotemporal<br>
prediction of<br>
> localized attacks carried out against
individuals in urban<br>
areas. We view<br>
> an attack as the outcome of a point
process governed by the<br>
interaction of<br>
> attackers, targets, and the physical
environment. Our<br>
ultimate goal is to<br>
> predict future outcomes of this process
in order to increase<br>
the security<br>
> of human populations and U.S. assets
and interests. However,<br>
achieving this<br>
> goal requires a deeper understanding of
how attack outcomes<br>
correlate with<br>
> the routine activities of individuals
in an area. The<br>
proposed research<br>
> will generate this understanding and in
doing so will answer<br>
questions such<br>
> as the following: What are the
dimensions along which<br>
individuals’<br>
> activities should be quantified for the
purpose of attack<br>
prediction? How<br>
> can measurements along these dimensions
be taken<br>
automatically and with<br>
> minimal expense (e.g., via social
media)? What are the<br>
implications of such<br>
> measurements for attack prediction
performance? Subsuming<br>
these questions<br>
> is the issue of geographic variation:
do our answers change<br>
when moving<br>
> from a major U.S. city to a major U.K.
city? There has been<br>
plenty of<br>
> previous work on spatiotemporal attack
prediction (see our<br>
Asymmetric<br>
><br>
Threat<<a moz-do-not-send="true"
href="http://ptl.sys.virginia.edu/ptl/projects/asymmetric-threat-prediction"
target="_blank">http://ptl.sys.virginia.edu/ptl/projects/asymmetric-threat-prediction</a>>project);<br>
> however, these basic questions remain
unanswered, leaving a<br>
> substantial gap in our understanding of
attack processes and<br>
their<br>
> relationships with individuals’ routine
activities.<br>
><br>
> More information can be found<br>
><br>
here<<a moz-do-not-send="true"
href="http://ptl.sys.virginia.edu/ptl/projects/routine-activities-analysis-for-crime-prediction"
target="_blank">http://ptl.sys.virginia.edu/ptl/projects/routine-activities-analysis-for-crime-prediction</a>><br>
> .<br>
><br>
> Sincerely,<br>
><br>
> Matthew S. Gerber, Ph.D.<br>
> Research Assistant Professor<br>
> Department of Systems and Information
Engineering<br>
> University of Virginia<br>
<br>
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<pre class="moz-signature" cols="72">--
Mike Scott
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