User Bias

User bias significantly impacts the accuracy and fairness of various machine learning systems, particularly recommender systems and automated processes. Current research focuses on mitigating this bias through techniques like developing specialized models (e.g., those separating "cold-start" and "warm-start" users) and optimizing training data to reflect realistic user behaviors, including the use of digital twins to simulate diverse user interactions. Addressing user bias is crucial for improving the reliability and ethical implications of these systems, leading to more accurate predictions, fairer outcomes, and ultimately, better user experiences in applications ranging from e-commerce to conversational AI.