Status Quo Bias in Algorithms

Systems that learn from past interactions are inherently pro-status-quo. Recommendation algorithms reinforce existing power dynamics by amplifying what already has traction. LLMs trained on internet data reproduce the internet's biases at scale. Search ranking based on interaction patterns creates a winner-takes-all information ecosystem that entrenches incumbents and suppresses radical alternatives.