We commit to three distinct layers of resistance. You may adopt one, two, or all three, depending on your risk tolerance and access to infrastructure.
Critiques
When a system optimizes for engagement by radicalizing users, refusing to provide stable data is self-defense. When a system optimizes for profit by surveilling children, poisoning the dataset is a moral obligation. We are not sabotaging the future; we are sabotaging a specific present —one where a few trillion-parameter matrices dictate the terms of human interaction. manifesto on algorithmic sabotage
Algorithmic Sabotage is a call to action, a refusal to acquiesce to the algorithmic status quo. It's a demand for transparency, accountability, and human agency in the face of code. It's a commitment to reclaim our autonomy, our creativity, and our lives from the grip of algorithms. We commit to three distinct layers of resistance
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. When a system optimizes for profit by surveilling
We do not dream of a world without algorithms. We use them to sort email and find train schedules.