The Hot Stove Effect: Why AI Learns to Be a Pessimist | HackerNoonThe Hot Stove Effect demonstrates how negative experiences affect learning biases.Underestimation errors persist due to reduced sampling of less favored alternatives.
Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Weight | HackerNoonAdaptive weight synchronization can mitigate biases in user learning by aligning training speeds based on performance metrics.