Property Finder fixes evaluation bias to unlock better models on Amazon Bedrock
Property Finder discovered that 96.8 percent of their evaluation labels were inherited from a single model's predictions rather than verified by human experts, creating systematic bias against alternative models. The team built an unbiased ground truth by manually annotating 356 images and shifted from single-label to multi-label classification. Using evolutionary prompt optimization on Amazon Bedrock, they achieved higher accuracy at 86 percent lower cost.
This case study provides a practical methodology for detecting and correcting evaluation bias in AI model assessment, demonstrating how to build unbiased ground truth and optimize prompts to select better models while significantly reducing costs.
Source: AWS Machine Learning Blog · aws.amazon.com