The proliferation of social media and e-commerce platforms has inadvertently provided a virtual haven for illegal wildlife trafficking and animal abuse. These platforms facilitate covert transactions and exchanges, allowing criminals to operate with a global reach while remaining relatively anonymous. Machine learning algorithms can actively monitor these platforms, flagging content that exhibits signs of potential wildlife crime or animal abuse, such as the sale of endangered species or the showcasing of cruel acts.
Wildlife preservation is a central focus for the impact investment company Wild Around, particularly in the realm of conservation technologies. In pursuit of this goal, Magnetic Core has conducted initial research into existing machine learning algorithms and strategies aimed at combating the trafficking of wildlife and instances of animal abuse. This research not only assesses the advancements made by current stakeholders but also research a possible model that could serve as a framework for formalizing efforts within the domain of social media platforms and e-commerce.
How to create a stable model that allows the identification of wildlife exploitation in images and videos and connects it to the moderation process
A well-balanced data categorization algorithm serves as the cornerstone for an ethical model that can be open-sourced for extensive utilization
Automatic identification of unethical content and moderation can assist law enforcement agencies and special institutions by providing data and possible evidence for exposure of organized crime groups, deeper investigation or legal actions against wildlife traffickers and abusers
Successful implementation can raise awareness about wildlife crime and animal abuse issues among the general public so they potentially stay away from sharing unethical content and report suspicious cases more often
Shared open-source algorithm and strategies can facilitate collaboration among different platforms, NGOs, and law enforcement agencies, fostering a united front against wildlife crime.
Automation frees up human moderators to focus on more complex cases, while machine learning handles routine tasks.