<div dir="ltr">If a clever system can predict who is going to predict a crime - with good, but far from 100%, accuracy, is the use it<div> a) rational policing practice</div><div> b) discriminatory</div><div>to use that information?</div>
<div><br></div><div>Seems to me, it's both.</div><div><br></div><div>Marek says</div><div>> <span style="font-family:arial,sans-serif;font-size:12.727272033691406px">But there are definitely many ways to abuse this technology as well.</span></div>
<div><span style="font-family:arial,sans-serif;font-size:12.727272033691406px"><br></span></div><div><span style="font-family:arial,sans-serif;font-size:12.727272033691406px">I don't feel abuse is the main issue. Any use of it is discriminatory. Should we trade off? Tough question.</span></div>
<div><br></div><div>Adam</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On 22 April 2014 11:34, Marek Rei <span dir="ltr"><<a href="mailto:marek.rei@gmail.com" target="_blank">marek.rei@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Here's an interesting article about how Chicago police is already applying such technology (in somewhat troubling ways):<div>
<br><div><a 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>
</div><div><br></div><div>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.</div>
<span class="HOEnZb"><font color="#888888">
<div><br></div><div>Marek</div><div><br></div></font></span></div></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><br><div class="gmail_quote">On Tue, Apr 22, 2014 at 10:55 AM, Christian Pietsch <span dir="ltr"><<a href="mailto:chr.pietsch@googlemail.com" target="_blank">chr.pietsch@googlemail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Matthew,<br>
<br>
so you want to build a heuristic precrime detector based on routine<br>
activities observed on social networks. Does that mean that if, say, I<br>
tend to update my status at the same time as some terrorist in your<br>
training set, your software will label me as a likely terrorist and<br>
put me on a no-fly list? Will I get a chance to prove my innocence?<br>
<br>
When you have some spare time, try to watch Minority Report. Or did<br>
this movie inspire your project? Then you must have misunderstood its<br>
message.<br>
<br>
Your suspect<br>
Christian<br>
<div><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 are seeking M.S.,<br>
> Ph.D., and post-doc researchers.<br>
><br>
> One-sentence summary: We are mining social media for indicators of<br>
> individual routine activities for the purpose of improved crime prediction.<br>
><br>
> Longer summary: This project focuses on the spatiotemporal prediction of<br>
> localized attacks carried out against individuals in urban areas. We view<br>
> an attack as the outcome of a point process governed by the interaction of<br>
> attackers, targets, and the physical environment. Our ultimate goal is to<br>
> predict future outcomes of this process in order to increase the security<br>
> of human populations and U.S. assets and interests. However, achieving this<br>
> goal requires a deeper understanding of how attack outcomes correlate with<br>
> the routine activities of individuals in an area. The proposed research<br>
> will generate this understanding and in doing so will answer questions such<br>
> as the following: What are the dimensions along which individuals’<br>
> activities should be quantified for the purpose of attack prediction? How<br>
> can measurements along these dimensions be taken automatically and with<br>
> minimal expense (e.g., via social media)? What are the implications of such<br>
> measurements for attack prediction performance? Subsuming these questions<br>
> is the issue of geographic variation: do our answers change when moving<br>
> from a major U.S. city to a major U.K. city? There has been plenty of<br>
> previous work on spatiotemporal attack prediction (see our Asymmetric<br>
</div>> Threat<<a 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>
<div>> however, these basic questions remain unanswered, leaving a<br>
> substantial gap in our understanding of attack processes and their<br>
> relationships with individuals’ routine activities.<br>
><br>
> More information can be found<br>
</div>> here<<a 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>
<div>> .<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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<br></blockquote></div><br><br clear="all"><div><br></div>-- <br>========================================<br><a href="http://www.kilgarriff.co.uk/" target="_blank">Adam Kilgarriff</a> <a href="mailto:adam@lexmasterclass.com" target="_blank">adam@lexmasterclass.com</a> <br>
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