Ghosts in the Feedback Loop

In the feedback loops of machine learning, certain bodies, cultures, and ways of knowing are made visible, distorted, or erased. Like ghosts—excluded yet ever-present—they haunt the machine. They slip through algorithmic blind spots, bend systems’ logic, and disrupt with glitches and ruptures that unsettle computational control.

But ghosts don’t just haunt—they rewire. They assert presence, reframe meaning, and speak in forms the system was never trained to understand. Through cracks in the loop, they reemerge as designers of new imaginaries for themselves and for the communities the system failed to see.