● Covered by 1 source · 1 reportMedium impact1 neutral
Property Finder fixes evaluation bias to improve AI models on Amazon Bedrock
Key points
- Property Finder found 96.8% of evaluation labels were biased.
- Bias stemmed from inheriting predictions, not human verification.
- Unbiased ground truth and prompt optimization were implemented.
- Achieved higher accuracy and 86% cost reduction on Amazon Bedrock.
Discovery of Evaluation Bias
Property Finder, a digital real estate application, identified a significant evaluation bias within their AI models. Their engineering team found that 96.8% of their evaluation labels were directly inherited from a single model's predictions, rather than being verified by human experts. This systematic skew made alternative models appear to underperform when measured against the existing ground truth.
Context of the Workload
Property Finder processes tens of thousands of property images daily across the Middle East and North Africa (MENA) region. Their system evaluates 11 quality attributes per image, including watermark detection, room classification, and photo quality. This assessment is crucial for thousands of new property listings, each with an average of 15 images, before they go live.
Migration and Optimization
The bias was uncovered during Property Finder's migration of their vision AI workload to a more cost-effective option on Amazon Bedrock. To address the issue, they built unbiased ground truth and applied an evolutionary prompt optimization methodology. This iterative process involved scoring predictions against an expert-annotated development set, performing error analysis, and refining prompts from v0 to v27, with later versions using automated optimization.
Results and Impact
By correcting the evaluation bias and optimizing their approach, Property Finder was able to select a more effective model on Amazon Bedrock. This resulted in achieving higher accuracy for their image classification tasks while simultaneously reducing costs by 86%. The primary challenge was not finding a better model, but rather identifying and correcting the flawed evaluation methodology itself.
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Primary sources
arXiv 2507.19457Reporting from
Property Finder discovered significant evaluation bias in their AI models, where 96.8% of evaluation labels were inherited from a single model's predictions instead of human verification. By building unbiased ground truth and using evolutionary prompt optimization on Amazon Bedrock, they achieved higher accuracy and 86% lower costs for their vision AI workload.