[Corpora-List] Open Research Position (M.S. / Ph.D. / post-doc): Analyzing Routine Activities for Crime Prediction

Mike Scott mike at lexically.net
Wed Apr 23 17:20:19 CEST 2014


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.)

Fascinating thread (nearly missed because of a most misleading subject line)

Cheers -- Mike

On 23/04/2014 09:44, M.E.Sciubba wrote:
> 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...
>
> Very interesting subject indeed, if I could only apply as a
> telecommuter post-doc ;)
>
> Cheers,
>
> Eleonora
>
> ____________________________________________________________________
>
> Dr. Maria Eleonora Sciubba
> Research Associate - Interactional Linguisuistics
> Archivio di LInguA Spontanea
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> 2014-04-23 5:57 GMT+02:00 Alexander Yeh <asy at mitre.org
> <mailto:asy at mitre.org>>:
>
> Zoltan Boka wrote:
>
> Predictions are only as good as the initial data they're based
> on. In
> this case that data could be incomplete or limited or biased (for
> instance, lets say that one data point is the number of
> arrests made on
> street x: lets also say that its police policy to harass and
> arrest
> people living on street x- you can see how this can infect the
> process.)
>
> But even if it can be done without bias and with a high
> accuracy, the
> question remains- are some people literally destined to commit
> crime and
> if so can that destiny be altered short of preemptively
> arresting them?
>
>
> 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.
>
>
> Sent from my iPhone
>
> On Apr 22, 2014, at 14:39, Adam Kilgarriff
> <adam at lexmasterclass.com <mailto:adam at lexmasterclass.com>
> <mailto:adam at lexmasterclass.com
> <mailto:adam at lexmasterclass.com>>> wrote:
>
> If a clever system can predict who is going to predict a
> crime - with
> good, but far from 100%, accuracy, is the use it
> a) rational policing practice
> b) discriminatory
> to use that information?
>
> Seems to me, it's both.
>
> Marek says
> > But there are definitely many ways to abuse this
> technology as well.
>
> I don't feel abuse is the main issue. Any use of it is
> discriminatory. Should we trade off? Tough question.
>
> Adam
>
>
> On 22 April 2014 11:34, Marek Rei <marek.rei at gmail.com
> <mailto:marek.rei at gmail.com>
> <mailto:marek.rei at gmail.com <mailto:marek.rei at gmail.com>>>
> wrote:
>
> Here's an interesting article about how Chicago police
> is already
> applying such technology (in somewhat troubling ways):
>
> http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist
>
> I wouldn't say crime prediction technology by itself
> is evil, it's
> more a question of how it's used. For example, I
> wouldn't have a
> problem with a system that can prioritise a large list
> of likely
> suspects after a crime has been committed, or is able
> to flag a
> social media message calling for a hate crime. But
> there are
> definitely many ways to abuse this technology as well.
>
> Marek
>
>
>
> On Tue, Apr 22, 2014 at 10:55 AM, Christian Pietsch
> <chr.pietsch at googlemail.com
> <mailto:chr.pietsch at googlemail.com>
> <mailto:chr.pietsch at googlemail.com
> <mailto:chr.pietsch at googlemail.com>>>
>
> wrote:
>
> Hi Matthew,
>
> so you want to build a heuristic precrime detector
> based on
> routine
> activities observed on social networks. Does that
> mean that
> if, say, I
> tend to update my status at the same time as some
> terrorist in
> your
> training set, your software will label me as a likely
> terrorist and
> put me on a no-fly list? Will I get a chance to
> prove my
> innocence?
>
> When you have some spare time, try to watch
> Minority Report.
> Or did
> this movie inspire your project? Then you must have
> misunderstood its
> message.
>
> Your suspect
> Christian
>
>
> On Mon, Apr 21, 2014 at 11:34:11AM -0400, Matthew
> Gerber wrote:
> > Hello,
> >
> > A new research position has opened within our
> lab, and we
> are seeking M.S.,
> > Ph.D., and post-doc researchers.
> >
> > One-sentence summary: We are mining social
> media for
> indicators of
> > individual routine activities for the purpose of
> improved
> crime prediction.
> >
> > Longer summary: This project focuses on the
> spatiotemporal
> prediction of
> > localized attacks carried out against
> individuals in urban
> areas. We view
> > an attack as the outcome of a point process
> governed by the
> interaction of
> > attackers, targets, and the physical
> environment. Our
> ultimate goal is to
> > predict future outcomes of this process in order
> to increase
> the security
> > of human populations and U.S. assets and
> interests. However,
> achieving this
> > goal requires a deeper understanding of how
> attack outcomes
> correlate with
> > the routine activities of individuals in an
> area. The
> proposed research
> > will generate this understanding and in doing so
> will answer
> questions such
> > as the following: What are the dimensions along
> which
> individuals'
> > activities should be quantified for the purpose
> of attack
> prediction? How
> > can measurements along these dimensions be taken
> automatically and with
> > minimal expense (e.g., via social media)? What
> are the
> implications of such
> > measurements for attack prediction performance?
> Subsuming
> these questions
> > is the issue of geographic variation: do our
> answers change
> when moving
> > from a major U.S. city to a major U.K. city?
> There has been
> plenty of
> > previous work on spatiotemporal attack
> prediction (see our
> Asymmetric
> >
>
> Threat<http://ptl.sys.virginia.edu/ptl/projects/asymmetric-threat-prediction>project);
> > however, these basic questions remain
> unanswered, leaving a
> > substantial gap in our understanding of attack
> processes and
> their
> > relationships with individuals' routine activities.
> >
> > More information can be found
> >
>
> here<http://ptl.sys.virginia.edu/ptl/projects/routine-activities-analysis-for-crime-prediction>
> > .
> >
> > Sincerely,
> >
> > Matthew S. Gerber, Ph.D.
> > Research Assistant Professor
> > Department of Systems and Information Engineering
> > University of Virginia
>
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-- Mike Scott

*** If you publish research which uses WordSmith, do let me know so I can include it at http://www.lexically.net/wordsmith/corpus_linguistics_links/papers_using_wordsmith.htm *** University of Aston and Lexical Analysis Software Ltd. mike.scott at aston.ac.uk www.lexically.net

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