HomeOffice of Sponsored ProgramsResearchArticle Display


The Limits of AI and Big Data Technology

  • Published
  • By JSOU

TOPIC SPONSOR: JSOU

The DoD  is placing a tremendous amount of faith in AI and Big Data approaches to complicated and wicked problems. However, data scientists recognize that the fundamental nature of challenges determine whether AI and Big Data are likely to produce the desired effects. What assumptions currently pervade military culture about AI and Big Data that, from a social science perspective, are inaccurate and counterproductive? What are the differences in AI and Big Data applications depending on whether the challenge is fundamentally an engineering question or a sociopolitical one? What are the limitations of AI and Big Data techniques in irregular warfare as a sociopolitical exercise and, therefore, their appropriate use as tools? What is the impact of limited data on training models and developing reliable tools, especially in sociopolitical applications? What is the impact on generating reliable training data results from needing answers quickly and without prior curation? What techniques could be employed to limit or prevent response bias unconsciously embedded in reporting that could skew results once fed into AI and Big Data analytical models? What techniques should be employed to ensure that data feeding AI and Big Data algorithms prevent confirmation bias due to biased reporting from prominent analytical frameworks diplomatic, information, military, and economic/political, military, economic, social, information, and infrastructure/areas, structures, capabilities, organization, people, and events, etc. or from cultural emphasis on certain factors at the expense of other, possibly more important, ones? How might social science methodology be taught to ensure AI and Big Data algorithms are populated with reliable data?

  • AU Library Libguide - Military Applications of Artificial Intelligence

  • Brode, Michael C., "Battle Management Automation: Balancing Technological Adoption and Trust with Risk," SAASS thesis, 2025, 95 pgs. 

    • This question asks what inaccurate and counterproductive assumptions currently pervade military culture regarding AI and data. Brode answers this by identifying "automation bias"—the dangerous and inaccurate assumption that a computer "system can never be wrong"—as a major cultural flaw. He explains that automated systems suffer from programming "brittleness" because they cannot always adapt to novel or unexpected operational variables. Brode warns that the military's assumption that automated systems can substitute for experienced, critically thinking crews artificially inflates the perceived output quality of the technology, leading to tragic outcomes when the system misidentifies targets.

  • Chapman, Maj. Kyle, "Artificial Intelligence: Fact, Fiction or Marketing?" ACSC elective paper, 2024, 12 pgs. 

  • Harding, Emily, Col. Matthew Strohmeyer and Mackenzie Richardson, "From Data to Insight: Making Sense out of Data Collected in the Gray Zone," CSIS brief, AFF paper, 2021, 17 pgs. 

  • Holloway, Maj. E. Minnenne and Maj. Bridget K. Pantaleon, "Rethinking Data Protection: AI, Big Data and Privacy in National Security," AF Fellows paper, 2024, 5 pgs. 

  • Klare, Capt. Christopher D., "Artificial Intelligence: Air Force Unprepared for 2025 Recommendations," SOS AUAR paper (AI) 2021, 12 pgs. 

  • Keith, Andrew J., "Alignment: National Security Objectives in Cold War Computer Simulations," SAASS thesis, 2025, 117 pgs.

    • Keith tackles this by refuting the common assumption within military culture that AI and logic-based computer simulations cannot represent "soft" sociopolitical objectives or irrational human behavior due to technological limits. He notes that while strategists often assume that simulation technology restricts the representation of ambiguous political goals, early models like TEMPER actually actively coded for psychological characteristics, biases, and cultural motivations to successfully simulate international relations. However, he highlights a counterproductive reality where these sociopolitical factors were unilaterally quantified by private contractors based on their own intuition rather than explicit policymaker input. Keith warns that treating sociopolitical modeling as a purely engineering challenge leads to opaque systems where the AI's embedded values and objectives misalign with those of the actual government sponsors and users.

  • Lewczyk, Jonathan A., "Maintaining Human Authority while Utilizing Artificial Intelligence in Commercial Aviation," AF Fellows paper (Georgetown, The Policy Issues of Big Data and Artificial Intelligence), 2023, 9 pgs. 

  • North, Matthew, "Can Machines Have Ethics?" AFGC thesis, 2025, 44 pgs.

    • North warns that AI/ML systems are entirely dependent on the historical data they are trained on, which leaves them highly vulnerable to human bias. For instance, if an AI is trained on historical human conflicts—such as the rapid shift in World War II from condemning the bombing of civilians to actively targeting them in firestorms—the algorithm may learn the wrong ethical lessons. Because AI only seeks the most efficient mathematical path to success, North argues that using flawed or biased human data could create a self-feeding cycle of unethical automated decisions.

  • O'Connor, Maj. Michael, "The Right Stuff for AI: Hard-Won Safety Lessons from the World of Flight Testing," AF Fellows research, published in Breaking Defense, December 1, 2023.