Analyzing the Problem Human Trafficking

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Human Trafficking isn’t something that numerous individuals think much about, so when adopting an interdisciplinary strategy and pulling two disciplines together to help recommend an answer can be extremely testing. Take law enforcement officers and their lack of education on the subject (Grubb and Bennett, 2012). The U.S has many laws that do not allow human trafficking as well as many policies.

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Even with that, many criminals still engage in this kind of illegal trafficking activity. With that being said, this huge problem happening all over not just the U.S. , but the world needs to be presented through an interdisciplinary standpoint.
Stating the Problem

Grubb and Bennett (2012) stated since the 1990s, human trafficking or ?trafficking in persons’ has been brought to the forefront of criminal justice issues, sparking international anti-trafficking effort among human rights proponents, governments entities and law enforcement agencies throughout the world. Human trafficking is still a problem and the current anti-trafficking efforts are just not enough in bringing down the criminals partaking in this. So what more is there that we can do to stop this? Cameras play a big role in just about the average person in today’s society. Smartphones cameras, traffic stop cameras. ATM cameras, and home security cameras are just few of the everyday cameras we come into contact on the daily. There is also high tech systems that are able to use facial recognition to track people. So with this type of technology in this day and age how are todays heroes, law enforcement, utilizing different systems and software’s to narrow down the whereabouts of victims and suspects involved?

Justifying an Interdisciplinary Approach

Human trafficking, has been a problem, and a hard one at that because it faces a threat to all social classes and no laws have currently made a drastic effect. With the advances in technology in the last decade it helps us in solving this problem. The discipline of computer science helps us help solve this issue through technology which is crucial. Organizational leadership is another discipline to help solve the problem because law enforcement can get the training they need to use the software and high tech surveillance systems. Mainly, they need to know how to use those systems as it pertains to this problem of human trafficking. With the integration of the disciplines, computer science and organizational leadership, true success can come out of lowering the illegal activity of human trafficking and finding victims beforetime runs out.

Identification of Relevant Disciplines

Many disciplines could have been used for Human trafficking, including but not limiting sociology and law for reasons that may seem obvious. For research on this specific focus question the disciplines of computer science and Organizational leadership applied better. According to Repko , by adequacy interdisciplinarians mean knowing enough about the discipline to have a basic understanding of how it approaches, as well as illuminates and characterizes, the problem (Repko, 2012, p. 102).

Computer science is essentially a type of software that is made to program a sequence of codes that then tells the hardware itself what to do. Computer science’s main principle, or one of them, is to tell a computer what needs to be done. Developers in the computer science realm create algorithms that help solve the problem. Which will allow the computer to read and execute the task. With this, developers can create more advanced surveillance technology and video processing. The computer science discipline doesn’t have much foundational theories that be applied to this. Most of the computer science research is experimental and falls under the STEM category.

Organizational leadership, which most consisted of quantitative studies for this paper, is making sure those who are the textbook definition of a leader are one. Ensuring that efficiency is at its max within. Guzman (2015) explained, leaders focus on their surroundings, encourage, and participate in activities within the group. This leadership style could help law enforcement in diminishing human trafficking.

Both disciplines can help law enforcement get the best software and training for suspects of human trafficking and victims using facial recognition as well as certain behavioral ones.

Analyzing the Problem

Problem Analysis

Discipline and Interdiscipline
Perspective stated in terms of an overreaching question asked

Computer Science

Will surveillance lead to a lack of privacy?

What is already available in terms of surveillance?

Can the software be improved for facial recognition and surveillance?

Organizational Leadership

Will law enforcement provide sufficient training on how to address human trafficking?

Will law enforcement implement what they used in training in the real world?

Are those chosen to train, capable of training officers on human trafficking?
Evaluation of Disciplinary Insights
Computer Science
Author
Insights Perspective
Assumption
Strength/Limitation of Insight
Lin, Seo, Gen, and Cheng (2009)

Using a 3D modeling algorithm, it can detect when behaviors were normal and also recognize when it was unusual
The research used can give a unique insight to the human trafficking problem.
Strength: The detection of unusual behavior can be used at largely populated areas like a football stadium or an airport.
Limitation: Can potentially detect something unusual as normal behavior
Szpak and Tapamo (2011)
Ships having an underwater surveillance system
The research used can give a unique insight to the human trafficking problem.
Strength: Surveillance can find Victims who are being smuggled through the sea in ships.
Limitation: Radar is not able to detect small ships
Hassaballah (2015)
The success seen within facial recognition is in controlled experiments
Research found that this would not be useful in Human trafficking.
Strength: Able to see if someone fits in the database
Limitations: Lighting can affect an image

Conflicts

Conflicts will occur when two different disciplines have a different perspective on a problem. As Repko states, it is essential to identify conflicts because it stands in the way of creating common ground and, thus, of achieving integration (Repko, 2012, p.294). In the Computer science case, the assumption is a persons face will be visible entirely by the cameras used within the recognition technology. All the research examined was done in a controlled environment for this research paper. In real life, faces may be partially covered or the lighting can significantly change the results of how successful it can be. This does contradict Lin’s article, because it has been stated how their successful experiment can be done In the real world.

The conflicts between the computer science and organizational leadership discipline is how the facial recognition software can lead to privacy issues.

Common Ground

Four techniques in reference to common ground are explained by Newell (2006). They are redefinition, extension, organization and transformation. Using those techniques, when explained about the problem of the system itself not working due to uncontrollable things such as lighting, include the improvements of such as improving the quality of the facial recognition system. If lead in the right direction, software as well as the product itself can be made better so that things such a lighting do not affect its intent. People have stated that it’s an invasion of privacy to have facial recognition, to find common ground, one would say your face is not private. When one willing is out in public, it is no longer private.

Interdisciplinary Outcome

Initially, it all began with the focus question, So with this type of technology in this day and age how are todays heroes, law enforcement, utilizing different systems and software’s to narrow down the whereabouts of victims and suspects involved? An answer to that question, there was an integration of the computer science and organization leadership insights. It was discovered during the research process that tracking surveillance was used before and some are currently still being tested but just not good enough to be taken into the real world yet. The integration of the insights of the two disciplines means the possibility of creating a better developed system and software and thus having leaders train those to use them to help identify and track suspects as well as victims of human trafficking.

More research is still needed to improve the algorithms that create the software of facial recognition. This would improve the accuracy and reliability of the software which would help aid in tracking of human traffickers and victims. With the integration of both disciplines explained in this research paper, systems could be a solution to the problem of human trafficking.

References

  1. Grubb, D., & Bennett, K. (2012). The readiness of local law enforcement to engage in US anti-trafficking efforts: an assessment of human trafficking training and awareness of local, county, and state law enforcement agencies in the State of Georgia. Police Practice &Research, 13(6), 487-500.
  2. Guzman, O. (2015) Organizational Leadership theories. Retrieved from https://smallbusiness.chron.com/organizational-leadership-theories-284.html
  3. Hadjimatheou, K. (2014). The relative moral risks of untargeted and targeted surveillance. Ethical Theory & Moral Practice, 17(2), 187-207.
  4. Hassaballah, M., & Aly, S. (2015). Face recognition: challenges, achievements and future directions. IET Computer Vision, 9(4), 614-626.
  5. Lin, L., Seo, Y., Gen, M., & Cheng, R. (2009). Unusual human behavior recognition using the evolutionary technique. Computers & Industrial Engineering, 56(3), 1137-1153.
  6. Lochner, S. A. (2013 Saving face: Regulating law enforcement’s use of mobile facial recognition technology and iris scans. Arizona Law Review, 55(1), 201-233.
  7. Repko, A. (2012). Interdisciplinary research: Process and theory. Los Angeles, CA, Sage.
    Szpak, Z., & Tapamo, J. (2011). Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set. Expert Systems With Applications, 38(6), 6669-6680.

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