The paper "AutoEval Done Right: Using Synthetic Data for Model Evaluation" by Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, and Michael I. Jordan introduces a method for efficient and statistically principled autoevaluation of machine learning models. The authors propose using AI-generated synthetic labels to reduce the need for human annotations, which can be costly and time-consuming. They develop algorithms that combine a small amount of human-labeled data with a large amount of synthetic data to improve the effective sample size of human data without compromising statistical validity. The core statistical tool used is prediction-powered inference (PPI), which is extended to PPI++ for better performance. The approach is demonstrated on various tasks, including evaluating model accuracy, metrics, and relative performance from pairwise comparisons. The results show that the proposed method can increase the effective sample size by up to 50% and provide more accurate point estimates and tighter confidence intervals compared to classical methods. The paper also discusses the broader impacts of AutoEval, including its potential to facilitate model evaluation in low-data regimes and improve algorithmic fairness.The paper "AutoEval Done Right: Using Synthetic Data for Model Evaluation" by Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, and Michael I. Jordan introduces a method for efficient and statistically principled autoevaluation of machine learning models. The authors propose using AI-generated synthetic labels to reduce the need for human annotations, which can be costly and time-consuming. They develop algorithms that combine a small amount of human-labeled data with a large amount of synthetic data to improve the effective sample size of human data without compromising statistical validity. The core statistical tool used is prediction-powered inference (PPI), which is extended to PPI++ for better performance. The approach is demonstrated on various tasks, including evaluating model accuracy, metrics, and relative performance from pairwise comparisons. The results show that the proposed method can increase the effective sample size by up to 50% and provide more accurate point estimates and tighter confidence intervals compared to classical methods. The paper also discusses the broader impacts of AutoEval, including its potential to facilitate model evaluation in low-data regimes and improve algorithmic fairness.