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AI Experts Rank Deepfakes and 19 Other AI-Based Crimes By Danger Level

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A new report published by University College London aimed to identify the many different ways that AI could potentially assist criminals over the next 15 years. The report had 31 different AI experts take 20 different methods of using AI to carry out crimes and rank these methods based on various factors. The AI experts ranked the crimes according to variables like how easy the crime would be to commit, the potential societal harm the crime could do, the amount of money a criminal could make, and how difficult the crime would be to stop. According to the results of the report, Deepfakes posed the greatest threat to law-abiding citizens and society generally, as their potential for exploitation by criminals and terrorists is high.

The AI experts ranked deepfakes at the top of the list of potential AI threats because deepfakes are difficult to identify and counteract. Deepfakes are constantly getting better at fooling even the eyes of deepfake experts and even other AI-based methods of detecting deepfakes are often unreliable. In terms of their capacity for harm, deepfakes can easily be used by bad actors to discredit trusted, expert figures or to attempt to swindle people by posing as loved ones or other trusted individuals. If deepfakes are abundant, people could begin to lose trust in any audio or video media, which could make them lost faith in the validity of real events and facts.

Dr. Matthew Caldwell, from UCL Computer Science, was the first author on the paper. Caldwell underlines the growing danger of deepfakes as more and more of our activity moves online. As Caldwell was quoted by UCL News:

“Unlike many traditional crimes, crimes in the digital realm can be easily shared, repeated, and even sold, allowing criminal techniques to be marketed and for crime to be provided as a service. This means criminals may be able to outsource the more challenging aspects of their AI-based crime.”

The team of experts ranked five other emerging AI technologies as highly concerning potential catalysts for new kinds of crime: driverless vehicles being used as weapons, hack attacks on AI-controlled systems and devices, online data collection for the purposes of blackmail, AI-based phishing featuring customized messages, and fake news/misinformation in general.

According to Shane Johnson, the Director of the Dawes Centre for Future Crimes at UCL, the goal of the study was to identify possible threats associated with newly emerging technologies and hypothesize ways to get ahead of these threats. Johnson says that as the speed of technological change increases, it’s imperative that “we anticipate future crime threats so that policymakers and other stakeholders with the competency to act can do so before new ‘crime harvests’ occur”.

Regarding the fourteen other possible crimes on the list, they were put into one of two categories: moderate concern and low concern.

AI crimes of moderate concern include the misuse of military robots, data poisoning, automated attack drones, learning-based cyberattacks, denial of service attacks for online activities, manipulating financial/stock markets, snake oil (sale of fraudulent services cloaked in AI/ML terminology), and tricking face recognition.

Low concern AI-based crimes include the forgery of art or music, AI-assisted stalking, fake reviews authored by AI, evading AI detection methods, and “burglar bots” (bots which break into people’s homes to steal things).

Of course, AI models themselves can be used to help combat some of these crimes. Recently, AI models have been deployed to assist in the detection of money laundering schemes, detecting suspicious financial transactions. The results are analyzed by human operators who then approve or deny the alert, and the feedback is used to better train the model. It seems likely that the future will involve AIs being pitted against one another, with criminals trying to design their best AI-assisted tools and security, law enforcement, and other ethical AI designers trying to design their own best AI systems.

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Blogger and programmer with specialties in Machine Learning and Deep Learning topics. Daniel hopes to help others use the power of AI for social good.