# Fix evaluation bias to unlock better models on Amazon Bedrock

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/fix-evaluation-bias-to-unlock-better-models-on-amazon-bedrock
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/fix-evaluation-bias-to-unlock-better-models-on-amazon-bedrock/)
> **Author**: Khaled Thabit (undefined)
> **Published Date**: Tuesday, October 27, 2026
> **Category**: INDUSTRY
> **Tags**: AI, AWS ML Blog, industry

## Executive Summary
Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. See how they built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate,...



## Full Coverage
This post was co-authored with Khaled Thabit, Meriem Boughrara, and Mohamed Desouky from Property Finder

The engineering team at Property Finder , a digital real estate application, discovered that 96.8 percent of their evaluation labels had been inherited directly from a single model's predictions rather than verified by human experts. Their ground truth was systematically skewed toward the incumbent model, making every alternative appear to underperform. The labels also had errors, but the bias was what blocked objective model comparison. They uncovered this when migrating their vision AI workload to a more cost-effective option on Amazon Bedrock

Property Finder processes tens of thousands of property images every day across the Middle East and North Africa (MENA). Their system evaluates 11 quality attributes per image. These cover watermark detection, room classification, furnishing status, photo quality, and construction indicators. That's thousands of new property listings with an average of 15 images each. All need quality assessment before going live

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