Sex-crime, trafficking and Machine Learning: Avengers at last


Can you trace a stolen soap to a sex-trade victim? What if, with AI, we could connect dots lurking in some dark streets that come alive in shadows? Like retail theft, ads, payment mechanisms and language?
This miracle is already happening. If you ask Prof. Eric Schles from New York University and researchers from some American universities, that is.
Some machine-learning algorithms shrink-wrapped in free suites could be just what the good guy needs in tracking and catching bad guys here. Just work with patterns in sex ads, pick cryptocurrency wallets and smoke out ring leaders of illegal prostitution that are operating online.
This echoes with what researchers Renata A. Konrad and others from Worcester Polytechnic Institute talk in a paper on how quantitative approaches can be used to crack trafficking networks, or to tap patterns in data, advertisements from traffickers on social media and other behavior insights.
ML becomes the superhero here by wielding matrix completion for cleaning up falsified information and filling in missing data. Even network analysis computational tools (and Naive Bayesian Classifiers) can help in taking the fizz off under-ground networks (and with automatic removal of online prostitution-postings).
Kudos. Now let’s take it further to child pornography-crackdowns please. Also use ML algorithms to dive beyond online pimps.
Wait, did we have to really use DeMo to fight Black Money alone?

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