Welcome to the next case example in the Computational Thinking course, Human Trafficking, a complex problem with complex solutions. I'm Darin Stockdill, the Instructional Design Coordinator at the Center for Education Design Evaluation and Research at University of Michigan School of Education. This course module deals with human trafficking, which is a serious problem in every part of the United States and across the globe. If you need help or want to get help for someone, reach out to the Human Trafficking Hotline at 1-888-373-7888. Why does computational thinking matter to human trafficking? Have you ever noticed those posters and stickers in bathroom stalls and on the walls of rest stops and other public places along highways? Did you ever wonder how they got there and why they were placed there? What kind of data had to be gathered to place them there? Who made those decisions and why? Those are some of the types of questions we're going to delve into when we think about how computational thinking can be used to tackle problems like human trafficking. Now, I am not an expert in human trafficking. I'm an instructional designer, curriculum developer, and educator. But I do focus a lot of my work on finding really complex social problems that give us case studies around which we can learn new skills and apply them to problem-solving. So we're going talk through this particular example of human trafficking, and think together about how computational thinking can be a valuable tool in helping combat this problem. I hope that my thinking out loud about the problem will help you consider new ways and applications for computational thinking as well. So let's start off with some definitions about human trafficking. What is it, and how can computational thinking help? Let's start with a basic definition. Human trafficking is modern-day slavery and involves the use of force, fraud, or coercion to obtain some type of labor or commercial sex act. Another definition states that human trafficking is a crime involving the exploitation of someone for the purposes of compelled labor or a commercial sex act to the use of force, fraud, or coercion. Let's think about the scale of the problem of human trafficking. We know it's massive. According to the anti-trafficking organization, the Polaris Project., The International Labor Organization estimated there were 40.3 million victims of human trafficking across the world in 2017. Eighty-one percent of these people were in situations of forced labor, 25 percent of them were children, and 75 percent were women and girls. The International Labor Organization also estimated that forced labor including sexual exploitation generates a $150 billion in profits annually for traffickers. So we can see we're talking about a problem that is absolutely massive and scale. In the United States, there were 40,987 cases reported to human trafficking hotlines between 2007 and 2017. There were 8,759 cases reported to hotlines in 2017. So you could see that this is a problem not just on the global level, but across the United States as well. It's also important to keep in mind that many, many cases of human trafficking are never reported. So the numbers that we're seeing are just conservative estimates of how massive this problem is. So what are the elements of human trafficking? How can we understand it as a set of practices in a little bit more detail? First you have to think about the act. What is actually done? Well, human trafficking involves recruitment, transportation, transfer, harboring or receipt of persons. We have to think about the means, how it is done. This recruitment transportation is done through threat or use of force, coercion, abduction, fraud, deception, abusive power, or vulnerability, or giving payments or benefits to a person who is in control of the victim. So why are these things done? They are done for the purposes of exploitation, which include exploiting the prostitution of others, sexual exploitation, forced labor, slavery, or similar practices, even including the removal of organs. Of course, this is a problem that centers upon people. When we think about the people impacted by human trafficking, we start off thinking about the victims and survivors. In the United States, human trafficking victims include men and women, adults and children, foreign nationals and US citizens. US law divides them into three primary groups; children under the age of 18 who are induced into commercial sex, adults aged 18 or over who are induced into commercial sex through force fraud or coercion, and children and adults who are induced to perform labor or services through force, fraud, or coercion. Human trafficking victims have been identified in cities, suburbs, and rural areas in all 50 states in Washington DC. So it is truly a national problem. When we think of the people again involved into human trafficking, we also have to think about the perpetrators. Based on human trafficking cases that have been identified by the National Human Trafficking Resource Center, traffickers may include brothel and fake massage business owners and managers, employers of domestic servants, gangs, criminal networks, growers and crew leaders in the agricultural sector, intimate partners and family members, labor brokers, factory owners and corporations, pimps, and small business owners and managers. So there's a large group of people exploiting others for both sexual exploitation and labor exploitation. The people involved in human trafficking on both ends of the equation are being involved across different places. So geographic location becomes important as well. What you see on the slide in front of you now is a key map of human trafficking activity across the world, and the brighter colors represent more activity. You see networks, you see particular nodes where trafficking is more intense. But you can see that it involves all corners of the world. If we think about the United States, this graphic just shows some of the locations where human trafficking task forces are active. This isn't the only place that human trafficking takes place, but there are particular hotshots where government agencies have set up task force to tackle the issue. Here is a different representation that shows us human trafficking cases reported to the human trafficking hotline in 2016. The darker the shade of brownish red, the more cases are being reported. But if you'll notice, there are no states where zero cases have been reported. At least one case has been reported in every state in 2016, and some states of course have a lot more. So can computational thinking help? What role do computers and computational thinking play in tackling this issue? Here's one example that's really interesting. There is a new set of digital apps for image analysis that are being employed to combat human trafficking through social media. One example is TraffickCam, an app that uses image analysis to map hotel rooms in an effort to identify the location of rooms that appear in social media posts and advertisements connected to human trafficking. Anybody can download the app, and you take pictures of any hotel room you stand while traveling. The app then creates a GPS location for that room and transforms the image of that room into a set of data points using room measurements, decor, carpet patterns, and other factors to create a unique way to identify and find specific hotel rooms in specific places. The images are then uploaded to a database, and law enforcement can use this database when they encounter trafficking advertisements including pictures of hotel rooms, and they can then cross-reference their image with the images in the database to try and identify the location. So here's a scenario to consider, that's going to help us think a little bit more about how computational thinking can help us address the issue of human trafficking. Imagine an operator working for the Polaris Project on their national hotline for human trafficking victims, and they receive a phone call. The caller is in a hotel room in an unfamiliar area. She's being forced into prostitution, but her abductor has left her alone briefly and she managed to get a hold of a cell phone. She wants to escape but she doesn't know where to go or what to do. All she knows is this hotline number, which she found on a sticker in a bathroom stall. The response, the hotline operator has a big challenge in front of them. She doesn't have time to waste. She needs to gather the right information in the least amount of time and figure out where the caller is with little information, and get her help without putting her in danger. This is really difficult because there are 215 different protocols or responses that the operator has to choose from, as well as 3,000 different resources across the country to which people can be referred. So cross referencing the protocols, the resources across the country, and all of the factors of this particular case becomes very challenging. So what's the solution? Polaris, ended up partnering with Palantir Technologies and they develop software with a user dashboard that allows the operator to quickly match the location and all the information about the situation with the most appropriate protocols and resources. The program also cues the operator in which additional information to gather and then tailors suggested solutions to the individual case. This is just one application of computational thinking being used to help address the issue of human trafficking. Here's a graphic representation of the situation I just described. Imagine that there are 75,000 calls to the Polaris human trafficking hotline on average every year. Each of those phone calls has a Google map location for the caller and its own content for a specific situation. There are then the 215 protocols to respond to each of those situations. Hotline workers then have to map all that information to the list of 3,000 resources, and each resource has a service profile. Each resource represents an organization that only does certain things. There is also then a physical Google map location for each resource. So the computer software helps pull all this information together, and helps the hotline worker manage the call.
