Algorithmic Targeting

In The You On AI Field Guide

Algorithmic targeting systems operate on the structure of AI outputs as plans. The system processes surveillance data and produces a classification: this pattern of signals or movements corresponds to a valid military target. The classification is a plan — a proposal about what the data means, based on statistical patterns in training material. Whether the classification is correct for the specific open-world situation it addresses depends on situated knowledge of the specific network, geography, and actors — knowledge that human intelligence analysts have traditionally built through years of working specific theaters.

The systems collapse the time available for evaluation. Traditional intelligence work involved slow, judgment-intensive assessment of ambiguous signals in context. Algorithmic targeting produces classifications faster than human operators can deliberate on them. The pressure to act on the output — to 'prosecute' the target, in the military vocabulary — intensifies. The situated judgment that distinguishes a reliable pattern from a training-data artifact is bypassed. The plan is treated as an action. As Suchman put it in her 2025 AI Now interview, 'the possibilities for judgment, for deliberation, for assessing the validity of the data... basically disappear.'

The consequences have been documented in multiple conflict theaters. In Gaza, the Israeli military's use of systems with names like Lavender, Gospel, and Where's Daddy has been the subject of extensive reporting and legal analysis, with Suchman among the scholars most prominently engaged in the critique. The pattern: AI systems generate target nominations at rates that preclude careful evaluation; human reviewers approve the nominations as procedural checkpoints rather than substantive assessments; civilian casualties accumulate as the structural consequence of speed and automation rather than of individual malice.

Suchman's analysis connects algorithmic targeting to her broader framework. The systems are not autonomous in any meaningful sense — they are sociomaterial assemblages of hardware, software, training data, operational procedures, and institutional pressures. But the reification of 'AI' as the active agent conveniently distributes responsibility: the AI 'chose' the target, the operator only 'approved.' The gap between plan and action is crossed not by situated judgment but by procedural compliance. The lethality is the structural consequence of treating a plan as an action at a tempo that makes evaluation impossible.

Origin

Algorithmic targeting has a history stretching back to Cold War-era pattern-matching over signal intelligence and has accelerated with every advance in computational capability. Contemporary systems draw on the machine learning revolution of the 2010s and on the surveillance infrastructure built out in the post-9/11 decades.

Suchman's specific engagement with the topic has deepened over the past decade, producing a series of essays and interventions in Social Studies of Science, open letters on autonomous weapons, and her 2025 AI Now Institute interview. The work has been cited extensively in international legal and policy discussions of AI in warfare.

Key Ideas

Targeting is classification. Algorithmic targeting systems produce classifications — plans about what surveillance data means — not adjudications of what should actually happen.

Speed eliminates deliberation. When outputs accumulate faster than humans can evaluate them, deliberation becomes procedural approval rather than substantive assessment.

Situated judgment is bypassed. The intelligence analyst's years of domain knowledge about specific networks, actors, and contexts is precisely what the automated system does not have and cannot replicate.

Distributed responsibility. The reification of 'AI' as the decision-maker conveniently distributes accountability; the assemblage of training data, corporate decisions, operational procedures, and individual operators is where accountability actually lives.

The template is general. The structure — AI generates plans, humans approve under time pressure, plans become actions without situated evaluation — applies beyond warfare to medicine, law, finance, and any domain deploying AI at scale.