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

Marek Rei marek.rei at gmail.com
Tue Apr 22 12:34:02 CEST 2014


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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