The U.S. is in a struggle to maintain its dominance in air, land, sea, space, and cyberspace over countries with capabilities increasingly on par in all domains with that of the U.S.  In addition, information (in all its forms) is the center of gravity of a broad set of challenges faced by the United States, including information operations, cyber information warfare, foreign hostile influence, intelligence operations, influence warfare, disinformation, proxy warfare, and the perennial threat of terrorism, foreign and domestic.

In response to these challenges, integration and interoperability have taken center stage, with organizations like the Department of Defense looking anew at their internal frameworks and architectures.  Innovation is imperative in areas such as preparedness, scenario planning/foresight strategy, strategic communications, climate resiliency, intelligence analysis, and mobilization. Viewed through a purely geopolitical lens, some are calling this period the ‘The Great Reset’ or the onset of another period in international relations marked by Great Power competition. China plays an integral role in all these scenarios.

Add a technology filter to this geopolitical viewpoint and the struggle becomes one for “Quantum Supremacy” or an “AI Arms Race” by “AI Superpowers.” Information, then, is the clear strategic vector of value creation for the emergence of applied technologies to enable operational innovation. For the U.S., the desired outcome is continued dominance for another American Century.  For the Chinese, military capabilities usher in the dawn of a new technological superiority and, as a result, geopolitical and military dominance on the world stage.

AI Systems and Intelligence Analysis

An initial response to this information gestalt that is the future of war and the key to successful iterative innovation, integration, and interoperability is the establishment of quantifiable metrics, inventive performance benchmarks, and clearly definable strategic outcomes (all driven by data).

The proper technological response by the intelligence community to these information-intensive challenges (and how intelligence gathering disciplines like SIGINT, GEOINT, and HUMINT will stay the same or undergo transformation) is the subject of research sponsored by the Office of the Secretary of Defense. The March 2021 report, “Evaluating the Effectiveness of Artificial Intelligence Systems in Intelligence Analysis,” was conducted within the Cyber and Intelligence Policy Center of the RAND National Security Research Division (NSRD).

An excerpt from the report illustrates the goal of the research and the final report:

“We focus on one portion of the problem: identifying metrics (alternatively, measures of performance) for AI systems that are adapted to the mission at hand. Using the academic AI literature, intelligence literature, and informal interviews with subject-matter experts across RAND and the government, this study develops a methodology for assessing the impact an AI system is likely to have on the intelligence mission that it supports and traces those impacts back to the properties of the system itself.”

“Both the calculated impact and the metrics that predict it can then be used to characterize the performance of the AI system in a way that informs decision-makers as to the actual value of the system to the mission. Though replicating human performance is sometimes cited as a sufficient criterion for success for an AI system and the most relevant threshold to cross before deploying a system, we argue that the applicability of this criterion is much narrower than it might initially appear.”

Areas For Further Research

The DoD/Rand report positions the following core research questions:

• How are AI system measures of performance connected with effectiveness in intelligence analysis?
• How might AI be used to support the intelligence process, both as reflected in the development of real systems and in hypothetical systems that may not yet be in development?
• How can researchers model the intelligence process for the purposes of determining how AI systems situated in this process affect it?
• What metrics exist to characterize the performance of AI systems?

Direct Link:  Evaluating the Effectiveness of Artificial Intelligence Systems in Intelligence Analysis | RAND

Related Reading:

China’s Strategy and Great Power Competition

For further insights into Great Power competition and the DoD innovation, integration and interoperability strategy, see: What the C-Suite needs to know about a Return to “Great Power Competition” and DoD Capabilities (per the Congressional Research Service

For more about China’s desire to shape a global technology and economic environment that is less influenced by a Western power, see: China’s Plan for Countering Weaponized Interdependence

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There are many ongoing research efforts by governmental entities, industry, AI think tanks and academia to frame the critical issues surrounding AI risk probability and impact. See:  “AI Accidents” framework from the Georgetown University CSES, as well as DHS (S&T) releases Artificial Intelligence (AI) and Machine Learning (ML) Strategic Plan Amidst Flurry of USG-wide AI/ML RFIs.

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