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

M.E.Sciubba mesciubba at gmail.com
Wed Apr 23 10:44:42 CEST 2014


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

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Dr. Maria Eleonora Sciubba Research Associate - Interactional Linguisuistics Archivio di LInguA Spontanea tel. +32 16 3 24795 cell +32 468177656

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2014-04-23 5:57 GMT+02:00 Alexander Yeh <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>> 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>> 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>>
>>>
>>> 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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