Artificial Intelligence Bias

Risks for Military Intelligence Operations

By CPT Tyrese Bender

Article published on: May 8, 2026 in the January-June 2026 edition of Military Intelligence

Read Time: < 8 mins

U.S Air Force photo by SSG Kaitlin Frazier altered and annotations added by MIPB staff.

Introduction

The rise of artificial intelligence (AI) in modern warfare presents profound opportunities and operational risks for the military intelligence (MI) community. While AI promises to enhance analytic speed and efficiency for MI, a critical vulnerability threatens these advantages: AI bias. AI bias refers to systematic errors in AI systems that yield inaccurate outputs due to unrepresentative training data, flawed design, or improper use.1 Empirical evidence, in both civilian and military contexts, reveals the extent to which AI bias can compromise AI-enabled targeting operations, enemy analysis, and intelligence sharing. In short, AI bias-related risks endanger not only mission success, but also servicemembers’ lives. To mitigate these risks, the Department of Defense (DoD) should consider implementing technical, operational, and institutional policy safeguards to ensure AI-enabled MI operations remain combat effective and ethically grounded.

Background

In recent years, the U.S. Government has accelerated the adoption of military AI capabilities to advance its national security objectives.2 This push comes as no surprise. AI has the potential to drastically transform military operations, and more specifically MI operations, by improving target identification, accelerating intelligence production, and strengthening decision-making support.3 In an era characterized by near-peer threats and rapid innovation, such operational efficiencies are national security imperatives. However, it is important to recognize that innovation and strategic necessity do not ensure operational advantage. History shows that breakthrough technologies present unforeseen risks on the battlefield. For example, during World War II, new radar technology failed to differentiate between friendly and enemy aircraft, providing U.S. forces with no early warning of the Pearl Harbor attack.4 Additionally, network-enabled warfare in the early 21st century created diffuse electromagnetic vulnerabilities that have rendered contemporary operations more susceptible to paralysis.5 AI’s integration into MI operations will likely introduce several similar bias-driven risks with the potential to compromise mission results and endanger U.S. Soldiers.

Risks for Military Intelligence

Evidence in both civilian and military contexts demonstrates how AI bias-driven misidentification can endanger human life. In 2019, U.S. researchers found that a wide range of AI recognition tools (e.g., facial recognition) relied on training data that was unrepresentative of the general population. This led law enforcement to misidentify, interrogate, and wrongfully arrest marginalized groups at disproportionate rates.6 As DoD programs develop similar AI recognition systems, these failures raise serious questions about the operational vulnerabilities AI bias poses.7 AI bias in military targeting operations may lead to high rates of misidentification in non-Western environments, where U.S.-centric data can skew results. Consequently, AI bias increases the risk of lethal strikes on incorrect targets, noncombatants, or even friendly forces. The Israeli Defense Force’s AI-enabled targeting in Gaza underscores the reality of these dangers, as AI bias-driven identification flaws resulted in unlawful interrogations and fatal strikes on Palestinian civilians.8 If AI bias errors were to similarly misguide U.S. targeting missions, the operational, humanitarian, and moral consequences would be severe.

AI bias vulnerabilities can also compromise MI analysis, where AI bias threatens to distort threat assessments and undermine decision making. In the U.S. justice system, AI bias has often informed inaccurate high-risk scores that contributed to harsher punitive sentences for marginalized citizens.9 Applied to MI, similar distortions could inform flawed assessments and biased intelligence pictures, prompting commanders to misallocate troops and resources based on false indications of high risk.10 At best, MI staff will waste time correcting skewed intelligence assessments; at worst, commanders may act on them. Such poorly informed decisions grant adversaries an advantage, undermine mission success, and expose servicemembers to greater danger. Just as flawed intelligence shaped the United States decision to invade Iraq, AI bias in intelligence will likely distort threat pictures, with equally grave consequences.

Compounding these challenges, AI bias also jeopardizes intelligence sharing between the United States and its allies. Effective intelligence collaboration can often foment strength in international partnerships and drive effective global responses to crises. The United States decision to disclose intelligence to Europe before Russia’s invasion of Ukraine demonstrated this well,11 but AI bias could introduce mistrust into these collaborative intelligence partnerships. Evidence already indicates that lack of trust is a significant obstacle to forming and maintaining U.S. intelligence-sharing relationships.12 Hence, as AI bias-driven mistrust continues to proliferate throughout the international community, allies may become hesitant to solicit or act on AI-enabled intelligence from the United States.13 Such impediments to the free flow of intelligence would leave America and its allies less informed, less unified, and less prepared for future crises. The likelihood and severity of these risks will only increase, especially as the DoD pushes to rapidly integrate AI without implementing effective safeguards against AI bias.

Recommendations

Mitigating these vulnerabilities will require the DoD to implement a coordinated policy approach comprised of effective technical, operational, and institutional safeguards against AI bias. Technically, the DoD should publish development and acquisition requirements that demand AI models be debiased and capable of producing transparency reports. As far as true debiasing is attainable, military AI models should draw from diverse, operationally relevant datasets that can adapt to dynamic battlefield conditions. The classification algorithms underlying these models should also account for region-specific social realities. AI models that rely on data reflecting local religions and that classify risk using metrics pertinent to regional cultures will likely result in fewer misidentifications and assessment errors. If uncertainty or malign influence arises, DoD AI models should also include transparency report functions, enabling users to validate the relevancy and sources of AI bias after the fact.

Operationally, the DoD should develop mandates and regulations to guide the ethical use of AI at the tactical level. Evidence indicates that maintaining human-in-the-loop requirements for targeting operations and intelligence production is often an effective safeguard against fallout from AI bias errors.14 In almost all cases, trained professionals need to maintain final authority over operational decisions affecting life and death, regardless of the efficiencies AI might offer. Importantly, the military needs to formalize these operational red lines, procedures, and exceptions within existing regulations to ensure servicemembers comply with AI bias-related mitigation measures. This can also help communicate an important operational tenet: AI can enable warfighting—but warfighters, not AI, should drive mission outcomes.

Implementing institutional measures can reinforce these technical and operational procedures. As AI use in the military becomes normalized, the DoD should require service-members to complete annual AI risk management training. Such training could help Soldiers counteract AI bias-related vulnerabilities before any operational consequences materialize. The DoD should also configure training programs to equip officers and senior enlisted leaders with the tools needed to manage the ethical use of military AI. Lastly, the DoD must, in coordination with the Office of the Director of National Intelligence and the National Security Council, develop international AI standards for military and intelligence operations. These measures can establish global standards for AI use in intelligence activities, counteract AI bias-driven mistrust, and enable intelligence-sharing relationships to flourish.

Conclusion

As the DoD looks to integrate AI into MI operations, it should recognize AI bias not just as a technical flaw but also as a strategic vulnerability. A large body of evidence in civilian and military literature points to the grave risks posed by AI bias to targeting operations, threat analysis, and U.S. intelligence-sharing relationships. To mitigate these vulnerabilities, the DoD must implement technical, operational, and institutional policies to protect against AI bias and ensure that AI delivers the operational edge it promises. With U.S. national security at stake, the choice is clear: the DoD must continue to adopt AI while forcefully addressing AI bias risks to secure America’s military advantage now and in the rapidly approaching future.

Endnotes