Pattern-of-life AI surveillance marks anyone with a 'suspicious' daily routine as a legitimate target

AI-powered pattern-of-life analysis ingests weeks of drone surveillance footage and cell phone metadata to identify individuals whose behavior patterns match 'militant' signatures (meeting locations, movement times, phone contacts). But in dense urban conflict zones, civilians who happen to follow similar patterns -- a baker who wakes at 4am and drives to multiple locations, a doctor who visits the same injured people -- get flagged as targets. The AI cannot distinguish between correlation and causation in behavioral patterns. This persists because pattern-of-life algorithms optimize for pattern matching, not causal understanding, and the training data labels (who is actually a militant) come from prior intelligence that itself may be wrong, creating a feedback loop of confirmation bias.