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Short abstract:

This article blends STS methods and expert interviews to challenge the notion that algorithmic technologies “increase certainty” of military decision-making. I unpack how certainty is co-constructed through pattern recognition, accuracy rates, verification procedures, and human judgment.

Long abstract:

Industries assert that novel algorithmic applications can unveil the fog of war and enhance certainty in military decision-making. Driven by the logic of risk reduction, militaries seek algorithms as solutions to mitigate uncertainties in warfare by organizing, categorizing, correlating, and making contemporary wars knowable in novel ways.

However, the use of algorithms to allegedly minimize the uncertainties of warfare introduces a new dimension of uncertainty into this sociotechnical assemblage. This dimension is related to the incomprehensibility of algorithmic processing – the so-called “black box” problem – and the evolving nature of these systems in ways that make it challenging to predict on what basis and how recommendations are made. To reinsert certainty into those systems, new procedures of verification and validation are developed to quantify the accuracy and reliability rates of algorithms.

Using STS-inspired methods, I study ‘uncertainty’ as a product that emerges through, in, and out of combined agencies of algorithmic processing, interface design, military professionals, military institutional logic and procedures, technologists, and legal frameworks. I blend desk research with expert interviews to grasp the practical implications of using software applications in the military context. With this methodology and focus, my piece contributes to an expanding body of literature in critical AI and international law studies that challenge the objectivity of algorithmic tools and examine with curiosity how new techniques and procedures provide novel non-legal ways of legitimizing their use in warfare.