Feedback Effect

Feedback effects, where a system's output influences its subsequent input, are a growing area of research across diverse fields, particularly in machine learning and human-computer interaction. Current studies focus on modeling these feedback loops mathematically, analyzing their impact on bias amplification, error propagation, and user engagement, often employing dynamical systems theory and investigating the effects of different feedback mechanisms (e.g., positive vs. negative, strong vs. weak). Understanding these feedback loops is crucial for improving the reliability and safety of AI systems, mitigating unintended consequences, and designing more effective and user-friendly intelligent agents.