By Vladimir Spinko
While AI is increasingly deployed in humanitarian, security, and disaster-response domains, the real challenge lies not in detection accuracy but in moral decision thresholds. This article examines how probabilistic AI systems translate uncertainty into life-critical actions, exposing hidden biases, accountability gaps, and the necessity of human oversight in high-risk environments.
Currently, most drone systems, whether used in humanitarian demining, disaster mapping or security surveillance, act primarily as monitoring tools. Their role is to gather sensor data (visual, thermal, radar, LiDAR, SAR, etc.) and present it to a human operator, who interprets the scene and makes decisions. The system’s value lies in extending human perception and reducing direct risk to operators; risk assessment, judgment, and decision-making remain human tasks.
AI systems deployed in high-risk humanitarian and security environments do not “know” that an area is safe. They operate on probabilistic inference. A drone-mounted ground-penetrating radar (GPR), thermal imager, or synthetic aperture radar (SAR) does not produce a binary output; it generates confidence intervals. A former minefield is not “clear”, it is classified as, for example, 98.2 % likely free of unexploded ordnance (UXO) based on sensor fusion and historical priors.
Yet, the ethical problem begins at the threshold: who decides whether 99 % confidence is sufficient, or whether 99.9 % is required? In humanitarian demining, the difference between these two numbers is not philosophical; it is operational. At 99 % confidence, one out of every 100 “cleared” zones may still contain a lethal device. At 99.9 %, that drops to one in 1,000 but the cost is non-linear. Survey time increases, sensor passes multiply, and operational budgets inflate rapidly.
The Black Box in the Field: Demining is context-dependent
The boundary between passive monitoring and autonomous action is increasingly blurred in security and demining operations. Traditional drone systems collect sensor data (visual, thermal, radar, LiDAR, SAR) for human operators to interpret, extending perception and reducing direct risk. Emerging applications, however, are moving toward systems that not only detect threats but reason about them and initiate action.
In security contexts, this shift is already apparent. In early November 2025, unauthorized drone incursions over European airspace forced temporary closures of Brussels and Liège airports, leading to dozens of cancelled or diverted flights and hundreds of stranded passengers. These incidents illustrate how sensor data can quickly escalate into high-stakes operational decisions, including airspace shutdowns, flight diversions, and emergency deployment of security personnel. In future deployments, AI may be tasked with evaluating whether a drone is hostile and determining the optimal response, effectively performing real-time risk assessment and action selection.
A similar evolution can be envisioned in demining. AI-enabled systems might integrate radar signatures, terrain models, and historical minefield data to compute probabilities that a given route contains unexploded ordnance. Based on these calculations, the system could recommend rerouting or flag high-risk zones, translating probabilistic assessments into operational decisions. In both domains, this represents a move from “what is” (detection of an object or anomaly) to “what should be done,” embedding normative judgments within machine reasoning.
Who Takes the Blame When AI Gets It Wrong And What Gets Lost When We Rush?
AI systems in high-stakes humanitarian contexts face a structural challenge: reproducing human decision-making where risk is non-linear and outcomes are irreversible. Human operators in demining, air traffic control, or evacuation coordination rely on tacit knowledge, pattern recognition, heuristics, and situational ethics. Two experienced deminers may assess the same terrain differently, and both outcomes can be operationally “successful,” yet these cannot be directly encoded as ground truth for AI.
Humanitarian demining and civil aviation, though working in very different environments, both rely on exceptionally detailed global standards designed to manage extreme, life-critical risks. These protocols – from step-by-step clearance procedures to tightly regulated maintenance and verification routines – exist precisely because even the smallest oversight can have catastrophic consequences. Yet history shows that even in the most regulated, checklist-driven industries, the probability of error is never zero. Conditions shift, edge cases emerge, and humans adapt in ways no guideline can fully capture.
In less regulated domains such as disaster relief, the uncertainty becomes even more acute. There are no globally standardized guidelines, decisions must be made within minutes, and information arrives incomplete or distorted. Drones play a crucial role in rapid damage assessment and victim search. Still, the cost of error is high: a misread thermal signature or an incorrectly flagged “safe” corridor can redirect rescuers away from those who need help most. In the chaos of an unfolding emergency, even a subtle algorithmic bias can escalate into a life-critical failure.
This creates a critical risk of bias in training data. Systems trained on historical minefields – for example, ordnance from past European conflicts – may miss improvised or novel devices in new theaters. AI that performs well on familiar terrain can become dangerously overconfident elsewhere. Auditing such hidden biases requires both technical validation against diverse datasets and contextual review by experienced operators.
The problem is amplified when decisions involve value trade-offs, such as exposing one operator to protect many civilians or delaying action to gather more data. Models that reduce these judgments to single metrics can obscure moral and operational complexity. The key question remains: how do we audit for hidden bias and ensure AI outputs are interpreted with human judgment, especially when the system’s confidence may give a false sense of certainty?
Human-in-the-Loop vs. Human-on-the-Loop: Ethical Oversight in Life-Critical AI
AI and automated systems face a unique trust dynamic: when a robot or algorithm errs, the perceived penalty is higher than for an equivalent human error. This phenomenon, known as algorithm aversion, has been documented in aviation and automation research, where a single automation failure reduces operator trust more sharply than a comparable human mistake. Even statistically sound AI recommendations may be questioned or rejected because the error seems opaque or the rationale is difficult to interpret.
From a moral and engineering standpoint, this raises the question of how humans should be integrated into decision loops. Should operators remain fully “in the loop,” approving every AI-generated action, or is it acceptable for them to be merely “on the loop,” monitoring decisions without direct intervention? In life-or-death safety contexts — whether clearing minefields, controlling air traffic, or directing emergency responses — maintaining a human “in the loop” is arguably ethically mandatory to ensure accountability and prevent catastrophic outcomes.
The broader concern is a slippery slope: today it is demining, tomorrow it could be AI assessing structural integrity after earthquakes, or planning evacuation routes during wildfires. Designing these systems requires embedding core ethical principles from the outset – including transparency, explainability, and explicit human oversight to prevent misuse or unintended harm. Without such safeguards, even highly accurate systems risk eroding trust and producing errors with consequences far beyond what their statistical performance might suggest.
From Mortal Consequences to Asset Loss: AI Risk Across Domains
There is a fundamental difference between systems used in life-critical domains and those used in security or interdiction tasks. In demining or air traffic control, a single error can directly translate into human death or serious injury. A false “safe” decision is irreversible. In these contexts, acceptable error rates approach zero, and every design trade-off is implicitly a moral decision about how much human risk can be tolerated.
The “hostile drone” problem operates under a different cost structure. If an innocent drone is misclassified and destroyed, the outcome is usually a financial loss – a $500-$2,000 asset written off not a human casualty. That asymmetry changes how risk is framed: systems are allowed to be more aggressive because the downside of error is economically acceptable when weighed against the potential threat to civilian aircraft. Treating both domains as equivalent hides this reality and produces dangerously misleading safety assumptions.
Decision-Making AI in Agriculture: Benefits and Minimal Consequences
Smart or precision agriculture illustrates a very different ethical and operational landscape. Unlike life-critical systems, AI here is often tasked not only with monitoring but also with decision-making, such as targeted fertilization, irrigation, or pest control. The advantage is clear: robots can optimize input use, reduce waste, and adjust treatments with high spatial precision, improving efficiency and crop yield.
However, the stakes of error are low. A misapplied fertilizer or missed weed patch rarely causes irreversible harm: the cost is usually economic or environmental, not human. Moreover, these systems operate in relatively well-understood domains with few input dimensions (soil moisture, nutrient levels, weather), and training paths are straightforward and highly supervised. In other words, precision agriculture AI is almost a “toy problem” compared with humanitarian or aviation applications: the margin for error is large, consequences are reversible, and the path from training data to safe deployment is relatively obvious.
Vladimir Spinko
