br markus
Am Dienstag, den 22.04.2014, 20:23 -0400 schrieb Zoltan Boka:
> 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?
>
> Sent from my iPhone
>
> On Apr 22, 2014, at 14:39, Adam Kilgarriff <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> 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> 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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> >
> >
> > --
> > ========================================
> > Adam Kilgarriff adam at lexmasterclass.com
> >
> > Director Lexical Computing Ltd
> >
> > Visiting Research Fellow University of Leeds
> > Corpora for all with the Sketch Engine
> > DANTE: a lexical database for English
> >
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