Unbiased Evaluation

Unbiased evaluation aims to develop methods for assessing the performance of machine learning models without the influence of inherent biases in data or evaluation metrics. Current research focuses on addressing biases in various domains, including anomaly detection, question answering, large language model ranking, and recommender systems, employing techniques like adjusted scoring metrics, debiasing data splits, and sample-efficient human evaluation strategies. These efforts are crucial for ensuring the reliability and fairness of machine learning systems across diverse applications, improving the trustworthiness of model performance assessments and ultimately leading to more robust and equitable AI.