Saturday, March 18, 2017

The Good Side of Tracking Algorithms

When massive amounts of seemingly unrelated data are subjected to analysis by really sophisticated computers or arrays of computers working together with even more sophisticated pattern analysis software, it is nothing short of amazing what can be generated. Some of it, reminiscent of the film Minority Report (a bit scary), has been harnessed successfully to generate where police can expect spikes in criminal activity… based on traffic patterns, police reports, weather patterns and extensive data based on decades of aggregated experience.
But finding patterns in billions, even trillions of events and communications, is what these systems are most aptly suited for. How they look at data, where they find correlations between the targeted behavior and the data-signs that such behavior is present or likely to be present is well exemplified by how investigations into human slavery, very young child labor and sex trafficking have benefited from this kind of analysis. This is valuable both in criminal investigations as well as for companies who want to make sure their supply chain is clean and above board.
“Recent United Nations estimates indicate that approximately 21 million people around the world are trapped in forced labor situations, including about 1.5 million in North America… Many of these people are being exploited in ways that have existed throughout history: About 22% are victims of ‘forced sexual exploitation,’ with others made to work in agriculture, manufacturing, construction, or domestic labor, according to the report from the U.N.’s International Labor Organization.
“Researchers and activists say part of the solution to this ancient problem may be surprisingly modern: Machine learning and similar statistical tools can identify suppliers of goods and services that are more likely to involve forced labor, whether they’re electronics manufacturers in developing countries or escort services in the United States. In the U.S., where sex work is frequently advertised online, leaving a digital trail, these techniques can also help guide law enforcement to sex trafficking gangs and their victims.
“In international trade, that kind of information can help buyers work with their vendors to ensure ethical practices throughout the supply chain or, failing that, switch to new vendors to stay in compliance with regulatory requirements and their own customers’ ethics.” March 12th.
But exactly what do they look at and how do they extract their conclusions? “Late last year, SAP Ariba began to let corporate customers track their risk of forced labor issues existing in their supply chains by integrating data from sources like the supply chain transparency service Made in a Free World. That, along with publicly available data like media reports of labor violations and assessments of labor issues in particular regions, allows the company to deliver comprehensible risk assessments even to companies with massively complex, multilevel supply chains.
“‘You can literally now look at your 60,000 suppliers, and you can see 200 of them have a high-risk exposure,’ [Alex Atzberger, president of SAP Ariba] says, offering a hypothetical example. ‘When you drill down, you can see how it computed a high-risk exposure based on multiple different factors.’
“Just making that data available doesn’t automatically eliminate labor issues, but it gives companies a starting point for working with vendors. With SAP Ariba, clients have direct access to data relating to potential suppliers, which is much more efficient than tracking down information on PDFs or in paper reports collecting dust in an isolated compliance office.”
But what about sex trafficking? “While even consensual sex work is illegal in most of the country, many people working in anti-trafficking efforts say they’re only interested in finding individuals forced into sex activity against their will and helping to prosecute those involved.
“‘Every day, there are hundreds of thousands of ads online that sell sex, and behind those are many victims of human trafficking,’ says Emily Kennedy, founder and CEO of Marinus Analytics, a Pittsburgh startup that grew out of the research Kennedy began when she was a student at Carnegie Mellon University.
“The company’s principal product is called Traffic Jam and is offered to law enforcement officials. It uses machine learning to analyze those sex trade postings through text and image analysis, with the goal of finding ads that share similarities with examples that had been previously flagged in trafficking cases. ‘It’s been used to start with one victim and find additional victims,’ says Kennedy. ‘In many instances, it will find different people pictured in the same location.’
“Authorities can also use Traffic Jam to follow ads tied to a clue like a name or a phone number, potentially finding sets of victims or advertisements linked to the same traffickers. Other organizations offer their own tools to law enforcement to address similar facets of the problem: Thorn, a California nonprofit founded by actors Demi Moore and Ashton Kutcher to combat child sex trafficking, offers a machine learning-powered tool that highlights escort ads that may be fronts for trafficking. The software is now used by about 4,000 officers across the U.S., according to Thorn CEO Julia Cordua.
“Identifying those ads on sight can sometimes be a challenge, especially since child sex traffickers will naturally claim all of their workers are above 18. They’ll also often avoid showing pictures of anyone who’s underage or coerced into labor, Cordua says. ‘It’s really difficult to eyeball it,’ she says. ‘We have trained different algorithms and we help them surface the ads that may be of highest risk.’” With artificial intelligence (AI), as arrests and verification statistics pour in to vindicate the statistical indicators, computer systems will be teaching themselves what to look for, refining their defining characteristics as the correlation between data and discovery mounts.
I’m Peter Dekom, and while there are deep privacy concerns about the intrusive nature of data analytics, there is another side that stands to help alleviate human suffering based on identifiable tags.

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