How Artificial Intelligence Can Help Stop Human and Organ Trafficking
Authors: Amina H Khalpey, PhD, Zain Khalpey, MD, PhD, Brynne Rozell BS, Bennett Gillespie, Torsten Trey, MD, PhD
Human trafficking and organ trafficking are some of the most heinous crimes plaguing the world. These crimes are clandestine, illegal, and notoriously difficult to track. In the 21st century, a lesser known but even more heinous and abominable act emerging is the practice of forced organ harvesting of living people, a euphemism of what could be described as transplant cannibalism. These are living people, mostly defenseless prisoners, who are killed on demand to harvest their organs for timely transplant surgeries. All of these grotesque forms of human trafficking have benefited from the rise of the internet has provided anonymous opportunities for traffickers and their associates with a new platform to exploit vulnerable individuals. To help combat this abusive practice which has derailed from its ethical foundation, machine learning algorithms can be used to track human and organ trafficking on the web and highlight irregularities that could help to identify state-organized forced organ harvesting.
Using Machine Learning To Recognize Patterns of Trafficking
Firstly, machine learning algorithms can be trained to identify patterns in online advertisements for prostitution or escort services. These patterns can be used to identify potential trafficking victims and traffickers. For example, if an ad features images of young or vulnerable-looking individuals, or if it uses language that suggests coercion or force, it may be flagged for further investigation. Secondly, machine learning algorithms can be used to analyze social media activity to identify potential trafficking victims. For example, victims may be coerced or threatened into posting specific messages or images on social media to signal their location or availability. Machine learning algorithms can analyze these patterns to identify potential victims and alert law enforcement. Thirdly, machine learning algorithms can be used to analyze online transactions related to organ trafficking. For example, if a large number of organ transplants are being facilitated through a particular website or payment platform, it may be a sign of organ trafficking. Machine learning algorithms can be used to identify these patterns and alert law enforcement. Fourthly, machine learning algorithms can be used to analyze transplant related data, including annual transplant numbers, number of transplant beds in hospitals, public organ donation numbers, societal willingness to donate organs, and others. This information can be used to calculate patterns that would help to produce a statistical probability for official transplant numbers for different countries. These reference points will allow investigators to either verify the officially provided annual transplant numbers of countries with a transparent transplant infrastructure or in other cases, especially where a lack of transparency prevents simple scrutiny, present the discrepancy of bad acting countries. The scientific contribution lies in the element that the lack of transparency, which could be bridged by artificial intelligence.
The Healthcare Community Should Work with Ai To Stop Human Trafficking
It is important to note that machine learning algorithms are not a panacea for human and organ trafficking or forced organ harvesting. They are a tool that can be used in conjunction with other methods of investigation and prevention. But artificial intelligence helps provide an important steppingstone in an area that is either opaque or victim to professional willful blindness. This obtuseness is in large part due to the fact that medical professionals might fear tapping into this inconvenient topic, instead of solving it head-on. The medical profession abides by a professional, ethical oath, which contributes to the foundation of its particular role in society. Violation of this professional oath demands a principled response, and actively correcting the wrongdoing of a few unethical offenders will certainly be rewarded by both patients and the public.
Artificial Intelligence Can Search The Dark Web For Trafficking
Human trafficking is called the “red market” because it is seen as a dark, underground market in which people are traded and exploited. The term is used to emphasize the illegal and morally reprehensible nature of the activity. It is also sometimes referred to as the “red light market” due to the association of red lights with the sex trade. This red market is exploited most easily using the dark web. In addition to analyzing the visible web, machine learning algorithms can also be used to track human and organ trafficking on the dark web. The dark web is a hidden aspect of the internet that is only accessible through specific software and protocols, making it difficult to track and monitor. This anonymity makes it an attractive platform for traffickers to advertise their illegal activities on the red market. One strategy machine learning algorithms can employ to trace trafficking on the dark web is by monitoring known trafficking hotspots and online forums. These algorithms can analyze the language and imagery used in the forums to identify potential trafficking victims and traffickers. For example, Ai may look for patterns of language that suggest coercion, violence, or exploitation. They may also look for images or videos that show individuals in vulnerable or unsafe situations. Another way machine learning algorithms can be used to monitor trafficking on the dark web is by analyzing financial transactions. Many trafficking activities on the dark web involve the exchange of cryptocurrency. Machine learning algorithms can monitor these transactions to identify potential trafficking activities. They can also look for patterns in transactions that suggest the involvement of particular individuals or organizations in trafficking.
Stopping Human Trafficking Requires an Interdisciplinary Team Effort
It is important to note that catching trafficking on the dark web requires specialized expertise and tools. Law enforcement agencies and anti-trafficking organizations need to work closely with data scientists and computer experts to develop and implement machine learning algorithms that can effectively track trafficking on the dark web. Healthcare workers also have an important role to play on the interdisciplinary team that is being built to halt this blight on society. Additionally, there are significant ethical and legal considerations that must be considered when conducting surveillance activities on the dark web. One of the most important ethical considerations that must be taken into account when using machine learning algorithms to monitor for trafficking is to respect the privacy of individuals who are not involved in trafficking.
Machine Learning Is a Powerful Tool to Catch Human Trafficking
In conclusion, machine learning algorithms can be a game changing strategy in the fight against human and organ trafficking and the forced organ harvesting of living persons. By analyzing patterns in online advertisements, social media activity, and online transactions, these algorithms can help law enforcement identify potential victims and traffickers. However, it is important to use these algorithms responsibly and ethically, and to combine them with other methods of investigation and prevention. Furthermore, machine learning algorithms can be used to track human and organ trafficking on the dark web. By monitoring trafficking hotspots, analyzing language and imagery, and tracking financial transactions, these algorithms can help identify potential victims and traffickers. However, this work requires specialized expertise and tools and must be conducted ethically and legally. Ultimately, machine learning is a powerful weapon in the arsenal that is being assembled in the fight against the villainy that is human and organ trafficking.
References Trey, T., Caplan, A.L. and Lavee, J. (2013) “Transplant Ethics under scrutiny – responsibilities of all medical professionals,” Croatian Medical Journal, 54(1), pp. 71–74. Available at: https://doi.org/10.3325/cmj.2013.54.71.