Artificial Intelligence for Counterterrorism?

The recent debate between the Associated Press and Facebook about the success of removing content posted by terrorist organizations should be a wake-up call concerning content moderation capabilities on these kinds of platforms. Facebook data indicates the removal of 99% of terrorism content, while AP contends that Facebook’s success is only 38%. The point here is that #machine_learning adds a limited capability to human content mediation. The current state of the art in #machine_learning in this area is far from meeting expectation and is a fantasy, created around the magical tool of #artificial_intelligence (AI).

Terrorist networks will continue to exploit advanced technology in the areas of social network mapping and terrorist recruitment to benefit from the #AI arms race. New #AI_technology in drones, among other things, will result in the production of cheap versions of them and that may easily fall into the hands of terrorists. There is no doubt that terrorist groups like ISIS will attempt to utilize all possible means to pursue terrorist activities. The gaps in content moderation in social media and communication networks will constitute opportunities for ISIS and others as well.

#Machine_learning has a technology aspect, a social context, and an industry dimension. On the one hand, it is a product of high technology and a market for it. The social context is where it impacts the daily lives of people. In this sense, there is a growing #AI intervention with an influence on the socio-economic conditions of people. This is an evolving phenomenon, which requires social, political, legal and ethical evaluations, in addition to technology.

#Machine_learning relies on algorithms that are known as classifiers. The classifier needs to be trained by data and works better if the difference in data, no matter how massive it is, clearly shows it. As it is fed by labeled categories, it is fragile against unforeseen conditions. It does not have a cognitive ability comparable to humans in this sense. That is why one would not expect #machine_learning to be able to respond to the complexities of societal and cultural value settings. The automated tools in one setting may be fragile in others. However, it is also next to impossible to monitor contents at today’s scale of social media and relevant platforms only with human capability. The need for #machine_learning is obvious.

The uploaders of content are aware of the deficits of machine learning enhanced tools. They develop measures to bypass the filters of automated tools. They may modify the content until they reach the goal of staying on the platform as much as possible. Human probes would help automated tools to discover blind spots. However, the idea of creating efficient filters may not always be possible. The industry dimension of machine learning does not like to disappoint customers. Providers may be faced with considerable fines/penalties if they cause government dissatisfaction in the case of benign posts. This situation results in over-filtering which puts machine learning to the side of “artificial” rather than the desired component of “intelligence” in content management.