: It aligns with "critical AI" perspectives that prioritize present-day harms—such as surveillance, labor exploitation, and racial bias—over speculative "existential risks". Drop #17. Manifesto On Algorithmic Sabotage
Machine learning models are brittle. The manifesto reminds us that adversarial inputs, feedback poisoning, and distributional drift can cripple systems that rely on clean data. This is empirically sound. manifesto on algorithmic sabotage