July 30 marked the United Nations’ World Day Against Trafficking in Persons, a day focused on ending the criminal exploitation of children, women and men for forced labor or sex work.
Between 27 and 45.8 million individuals worldwide are trapped in some form of modern-day slavery. The victims are forced into slavery as sex workers, beggars and child soldiers, or as domestic workers, factory workers and laborers in manufacturing, construction, mining, commercial fishing and other industries.
Human trafficking occurs in every country in the world, including the U.S. It’s a hugely profitable industry, generating an estimated US$150 billion annually in illegal profits per year. In fact, it’s one of the largest sources of profit for global organized crime, second only to illicit drugs.
Analytics, the mathematical search for insights in data, could help law enforcement combat human trafficking. Human trafficking is essentially a supply chain in which the “supply” (human victims) moves through a network to meet “demand” (for cheap, vulnerable and illegal labor). Traffickers leave a data trail, however faint or broken, despite their efforts to operate off the grid and in the shadows.
There is an opportunity – albeit a challenging one – to use the bits of information we can get on the distribution of victims, traffickers, buyers and exploiters, and disrupt the supply chain wherever and however we can. In our latest study, we have detailed how this might work.
Finding people at risk
In most countries, resources to fight human trafficking are woefully inadequate. Agencies strive to use them as effectively and efficiently as possible, and often find themselves fighting for scarce funding and support. A government, for example, may need to decide how best to fund or schedule labor inspectors to detect child labor in the manufacturing industry. An organization with limited resources may need insight into which prevention program to run, or what type of awareness campaign to implement.
We can use data to identify populations most at-risk and target prevention campaigns to those populations. Risk factors for being drawn into trafficking include poverty, unemployment, migration and escape from political conflict or war. Experiences with organized crime and natural disasters can also change to a person’s risk.
Trafficking often begins with fraudulent recruitment methods, such as promises of employment or romance. Data can help identify specific economically depressed areas, where we can deploy awareness campaigns and social service support.
In operations research, scientists apply mathematical methods to answer complex questions about patterns in data and predict future trends or behaviors. Analytical tools similar to those used in transportation, manufacturing and finance can help us decide where to best allocate resources and help locate shelters for victims.
Victim identification and location
Trafficking networks are dynamic. Traffickers are likely to frequently change distribution and transportation routes to avoid detection, leaving law enforcement and analysts with incomplete information as they attempt to identify and dismantle trafficking networks.
However, researchers can help by tracking subtle trends in data at various locations; at access points where we actually come in contact with victims, such as the emergency room; and in the activity of local law enforcement.
In the sex trade, for example, clues may be found in patterns of petty theft, by looking at transactional data from purchases at retail outlets. Victims sometimes steal essential supplies that traffickers may not provide for them such as feminine hygiene products, soap and toothpaste. Trends in the use of cash for transactions normally made with debit or credit cards – hotel bookings, for example – may also raise a red flag.
Traffickers advertise on social media and internet-based sites. Analytics could seek patterns in photos through facial recognition software, comparing images from missing person reports or trafficking ads.
Sex trafficking activity, in particular, leaves traces in the public areas of the internet, mostly in the form of advertisements and escort ads. Advertisers tend to use social networks and dating websites, while more proficient traffickers frequently alter their online presence to try to elude identification.
Machine learning – a type of artificial intelligence where computers teach themselves to do tasks, such as recognize images – can be used to detect online trafficking activity. Recent advances in matrix completion, a type of machine learning, could even help clean up falsified information or make predictions about missing data.
Traffickers are also known to take advantage of increased demand for commercial sexual exploitation during major events, including conventions and large sporting events. Analyses that look at both location and timing of online ads could help law enforcement detect and possibly interdict transportation of victims to the event. They could also suggest when and where policymakers should focus intervention efforts.
Interrupting the flow of people, money and other components of trafficking is critical to identifying trafficking networks, disrupting their infrastructure at the source and eliminating them.
Unfortunately, network interruption requires the cooperation of authorities and the public surrounding the network. In some countries, such as Nepal and Costa Rica, officials are threatened or bribed into ignoring or otherwise allowing human trafficking. There is often inadequate regulatory oversight of industries known to use trafficked laborers. Traffickers can easily fabricate or alter a victim’s identification documents, rendering them invisible to overburdened authorities.
To help authorities identify trafficking operations to target, researchers could turn to network analysis, a mathematical way of representing real world systems and their interactions. For example, network analysis can be used to map out the dynamics of users and their connections embedded in social networks, such as Facebook and Twitter. This can possibly identify at-risk persons or, alternatively, traffickers or customers.
Social network analysis could also help to determine which contacts have a critical influence over others. This may enable early identification of either a victim or trafficking transaction.
Human trafficking is a serious crime and an appalling violation of human rights. Almost every country is affected by human trafficking as a source of victims, a transit point, or a destination and location of abuse. These new mathematical tools show great potential both to interrupt the human trafficking cycle and to provide the information needed to help victims escape to safety.
Renata Konrad, Assistant Professor of Operations and Industrial Engineering, Worcester Polytechnic Institute and Andrew C. Trapp, Associate Professor of Operations and Industrial Engineering, Worcester Polytechnic Institute
Siku ya kimataifa ya kupambana na biashara haramu ya binadamu (World Day Against Trafficking in Persons) ni siku ya kimataifa ya kupambana na biashara haramu ya binadamu, ambayo inazingatia kukomesha utumiaji wa uhalifu wa watoto, wanawake na wanaume kwa kazi ya kulazimishwa au kazi ya ngono. Watu milioni 27 hadi 45.8 ulimwenguni pote wanakabiliwa na utumwa wa kisasa. Waathirika wanalazimishwa kuwa watumwa kama wafanyakazi wa ngono, waombaji na askari watoto, au kama wafanyakazi wa nyumbani, wafanyakazi wa viwanda na wafanyakazi katika viwanda, ujenzi, madini, uvuvi wa kibiashara na viwanda vingine. Uhamiaji wa binadamu unafanyika katika kila nchi duniani, ikiwa ni pamoja na Marekani, ni sekta yenye faida kubwa, inayozalisha takriban dola bilioni 150 za Marekani kila mwaka katika faida haramu kwa mwaka. Kwa kweli, ni mojawapo ya vyanzo vikubwa zaidi vya faida kwa uhalifu uliopangwa ulimwenguni pote, wa pili tu baada ya dawa za kulevya haramu. Uchambuzi, utafutaji wa hisabati wa ufahamu katika data, unaweza kusaidia utekelezaji wa sheria kupambana na biashara ya binadamu. Uhamiaji wa binadamu kimsingi ni mlolongo wa usambazaji ambapo <unk>usambazaji<unk> (wajeruhi wa kibinadamu) husafiri kupitia mtandao ili kukidhi <unk>mahitaji<unk> (kwa kazi ya bei nafuu, dhaifu na haramu). Wafanyabiashara wa binadamu huacha alama ya data, hata iwe dhaifu au imevunjika, licha ya juhudi zao za kufanya kazi nje ya mtandao na katika kivuli. Kuna fursa - ingawa ni changamoto - ya kutumia vipande vya habari tunavyoweza kupata juu ya usambazaji wa waathirika, wafanyabiashara, wanunuzi na watumiaji, na kuvuruga mlolongo wa usambazaji popote na kwa njia yoyote tunayoweza. Katika utafiti wetu wa hivi karibuni, tumeelezea jinsi hii inaweza kufanya kazi. Katika nchi nyingi, rasilimali za kupambana na biashara haramu ya binadamu ni za kutosha sana. Mashirika hujitahidi kuzitumia kwa ufanisi na ufanisi iwezekanavyo, na mara nyingi hujikuta wakipigana kwa ufadhili na msaada mdogo. Serikali, kwa mfano, huenda ikahitaji kuamua jinsi bora ya kufadhili au kupanga ratiba ya wakaguzi wa kazi ili kugundua kazi ya watoto katika sekta ya utengenezaji. Shirika lenye rasilimali chache linaweza kuhitaji ufahamu wa programu gani ya kuzuia inayoweza kuendeshwa, au aina gani ya kampeni ya ufahamu ya kutekeleza. Tunaweza kutumia data kutambua idadi ya watu walio hatarini zaidi na kulenga kampeni za kuzuia kwa idadi hiyo ya watu. Vitu vinavyosababisha watu kuuzwa ni umaskini, ukosefu wa ajira, uhamiaji na kutoroka vita vya kisiasa. Uzoefu na uhalifu uliopangwa na majanga ya asili pia inaweza kubadilika kwa hatari ya mtu. Mara nyingi biashara haramu huanza kwa kutumia mbinu za udanganyifu za kuajiri watu, kama vile ahadi za kazi au mahusiano ya kimapenzi. Takwimu zinaweza kusaidia kutambua maeneo maalum ya kushuka kwa uchumi, ambapo tunaweza kupeleka kampeni za ufahamu na msaada wa huduma za kijamii. Katika utafiti wa shughuli, wanasayansi hutumia mbinu za hisabati kujibu maswali magumu kuhusu mifumo katika data na kutabiri mwenendo wa baadaye au tabia. Vifaa vya uchambuzi vinavyotumika katika usafirishaji, utengenezaji na fedha vinaweza kutusaidia kuamua ni wapi bora kuwekeza rasilimali na kusaidia kupata makazi kwa waathirika. Utambulisho na mahali pa waathirika Mitandao ya biashara haramu ni yenye nguvu. "Washambuliaji wa binadamu mara nyingi hubadilisha njia za usambazaji na usafirishaji ili kuepuka kugunduliwa, na kuacha maafisa wa kutekeleza sheria na wachambuzi na habari isiyo kamili wakati wanajaribu kutambua na kuvunja mitandao ya usafirishaji.""" Hata hivyo, watafiti wanaweza kusaidia kwa kufuatilia mwenendo wa data katika maeneo mbalimbali, katika vituo vya ufikiaji ambapo sisi kwa kweli kuja katika kuwasiliana na waathirika, kama vile chumba cha dharura, na katika shughuli za kutekeleza sheria za mitaa. Katika biashara ya ngono, kwa mfano, dalili zaweza kupatikana katika mifumo ya wizi mdogo, kwa kuangalia data za shughuli kutoka kwa ununuzi katika maduka ya rejareja. Wakati mwingine waathirika huiba vifaa muhimu ambavyo wafanyabiashara hawawezi kuwapatia kama vile bidhaa za usafi wa wanawake, sabuni na dawa ya meno. Mwelekeo katika matumizi ya fedha taslimu kwa ajili ya shughuli kawaida kufanyika na kadi ya debit au mikopo <unk> hoteli bookings, kwa mfano <unk> inaweza pia kuinua bendera nyekundu. Wafanyabiashara wa binadamu hutangaza kwenye mitandao ya kijamii na tovuti za mtandao. Utafiti wa picha unaweza kutafuta mifumo katika picha kupitia programu ya utambuzi wa uso, ukilinganisha picha kutoka kwa ripoti za watu waliopotea au matangazo ya biashara haramu. Uhamiaji wa ngono, hasa, huacha alama katika maeneo ya umma ya mtandao, hasa katika fomu ya matangazo na matangazo ya kuandamana. Watangazaji hutumia mitandao ya kijamii na tovuti za uchumba, wakati wafanyabiashara wenye ujuzi zaidi mara nyingi hubadilisha uwepo wao mtandaoni kujaribu kuepuka utambulisho. Mafunzo ya mashine - aina ya akili bandia ambapo kompyuta hujifundisha kufanya kazi, kama vile kutambua picha - inaweza kutumika kugundua shughuli za biashara haramu mtandaoni. Maendeleo ya hivi karibuni katika kukamilisha matrix, aina ya kujifunza mashine, inaweza hata kusaidia kusafisha habari bandia au kufanya utabiri kuhusu data inayokosekana. Wafanyabiashara wa binadamu pia wanajulikana kwa kutumia fursa ya kuongezeka kwa mahitaji ya unyonyaji wa kingono wa kibiashara wakati wa matukio makubwa, ikiwa ni pamoja na makusanyiko na matukio makubwa ya michezo. Uchambuzi wa eneo na wakati wa matangazo ya mtandaoni unaweza kusaidia kutekeleza sheria kugundua na uwezekano wa kuzuia usafirishaji wa waathirika kwenye tukio hilo. Pia wangeweza kupendekeza ni lini na wapi watunga sera wanapaswa kuzingatia juhudi za kuingilia kati. """Kuvunja mtiririko wa watu, fedha na vipengele vingine vya biashara haramu ni muhimu kwa kutambua mitandao ya biashara haramu, kuvuruga miundombinu yao kwenye chanzo na kuiondoa." Kwa bahati mbaya, kukatizwa kwa mtandao inahitaji ushirikiano wa mamlaka na umma kuzunguka mtandao. Katika nchi kama vile Nepal na Costa Rica, maafisa wanatishwa au kupewa rushwa ili kupuuza au kuruhusu biashara ya binadamu. Mara nyingi kuna usimamizi duni wa viwanda vinavyojulikana kutumia wafanyakazi waliouzwa. Wafanyabiashara wanaweza kutengeneza au kubadilisha kwa urahisi hati za utambulisho za mhasiriwa, na kufanya iwe haiwezekani kwa mamlaka zilizo na mzigo mwingi. Ili kusaidia mamlaka kutambua shughuli za biashara haramu, watafiti wanaweza kugeukia uchambuzi wa mtandao, njia ya hisabati ya kuwakilisha mifumo ya ulimwengu halisi na mwingiliano wao. Kwa mfano, uchambuzi wa mtandao unaweza kutumika kuchora mwenendo wa watumiaji na uhusiano wao ulioingizwa katika mitandao ya kijamii, kama vile Facebook na Twitter. Hii inaweza uwezekano wa kutambua watu katika hatari au, mbadala, wafanyabiashara au wateja. Uchambuzi wa mitandao ya kijamii unaweza pia kusaidia kuamua ni mawasiliano gani yanayoathiri sana wengine. Hii inaweza kuwezesha utambulisho wa mapema wa mwathirika au shughuli ya biashara haramu. Uuzaji haramu wa binadamu ni uhalifu mkubwa na ukiukaji mkubwa wa haki za binadamu. Karibu kila nchi inaathiriwa na biashara haramu ya binadamu kama chanzo cha waathirika, kituo cha usafiri, au marudio na eneo la unyanyasaji. Vifaa hivi vipya vya hisabati vina uwezo mkubwa wa kuvunja mzunguko wa biashara ya binadamu na kutoa habari zinazohitajika kusaidia waathirika kutoroka kwa usalama. Renata Konrad, Profesa Msaidizi wa Uendeshaji na Uhandisi wa Viwanda, Chuo Kikuu cha Worcester Polytechnic, na Andrew C. Trapp, Profesa Msaidizi wa Uendeshaji na Uhandisi wa Viwanda, Chuo Kikuu cha Worcester Polytechnic.