\u201c\u2026the Intelligence R&D Council formed the Artificial Intelligence Steering Group (AISG) for the expressed purpose of providing a mechanism for exchanging information throughout the Community regarding Artificial Intelligence (AI)\u201d[1]. Americans, who tend to view government as a slow-moving, backwards bureaucracy, may be surprised to learn these words are from a 1984 CIA memo summarizing findings from a CIA-backed conference about intelligence applications of new computing techniques. Given recent technological leaps made in information gathering and processing technologies, one wonders: how is machine learning changing spycraft at the CIA?<\/p>\n
An economic framework used by scholars Ajay Agrawal, Joshua Gans, and Avi Goldfarb to assess the impact of predictive analytics provides insight. Machine learning represents a decrease in the price of prediction and will increase demand for complements to prediction, such as judgment and creativity[2]. Since the CIA\u2019s job is to gather intelligence, analyze it, and use the results to predict actions of foreign actors, it follows that new computational tools powered by machine learning will reduce time CIA analysts and operatives spend processing data and increase time spent drawing conclusions from it. To this end, the CIA is investing financially and organizationally to develop strong data processing and analytics capabilities.<\/p>\n
In the short-term, the CIA is investing in both external and internal innovation. In 1999, the CIA chartered In-Q-Tel, a prestigious VC firm established to invest in technologies with national security applications[3]. Some notable portfolio companies include Palantir, whose data mining software is used across public and private sector institutions, and Keyhole, a mapping firm acquired by Google[4]. Today, the CIA has close to 140 machine learning and artificial intelligence projects underway[5]. In a real world spy-vs-spy case, one project helps agents avoid detection with a tool that maps cameras in foreign capitals by combining public images from Google Street View with algorithms trained to recognize cameras[6]. Another project uses open-source datasets to develop indicators of significant political and social events; the resulting tool predicted protests in Mexico triggered by an election and similar outbreaks in Paraguay connected to the impeachment of a former president[7]. A third project, sponsored by a different government agency but of benefit to the CIA, increases the amount of imagery an analyst can process by 95%[8].<\/p>\n
In order to prepare for long-term changes to the intelligence trade, the CIA is changing itself structurally. On October 1st<\/sup>, 2015, in recognition of the need to build greater competency in digital approaches to data collection, management, and analysis, the CIA made its biggest organizational change since 1963 and established the Directorate of Digital Innovation (DDI) [9]. The Directorate is tasked with solving three problems: first, helping agents hone digital hacking and sleuthing skills; second, improving the CIA\u2019s data management systems; and third, building predictive tools that leverage all of the CIA\u2019s data[10]. According to CIA Deputy Director Andrew Hallman, who leads the new Directorate, \u201cthe days of attending a cocktail party and writing up your notes are over\u201d[10]. Does this mean an end to human spycraft? According to Dawn Meyerriecks, CIA Deputy Director for Science and Technology, computation leads not to an end of human analysis, but one in which machine learning supercharges human capabilities[11].<\/p>\n While the information processing and predictive abilities provided by machine learning tools will improve CIA\u2019s spying abilities, it is important to remember other nations are embracing these same technologies. As a result, CIA spies deployed abroad can be tracked remotely without human tails; in China, for example, image recognition powers cameras that blanket cities and can find fugitives in a crowd of 60,000 people[12]. Ironically, digitally enabled adversaries will necessitate the need for more, not less human spycraft, a view echoed by Jason Matthews, a CIA veteran and author of the Red Sparrow series [13]. The CIA will need human spies to create emotional bonds with potential agents, to manage their work, and perhaps most importantly, to compromise the digital systems and tools employed by adversaries.