Ideally, once a property is assessed to be in good condition, an insurer could fast-track its approval. But without a measure of confidence in that assessment, how can the insurer be certain? Poor image quality could cause the AI to overlook a major defect in the roof. This is when a confidence score becomes necessary. The Betterview confidence score determines not only how well our AI performs on an image, but how well that image actually reflects the real property. Let’s consider an example. Looking at the image below, is it possible to be sure there isn’t serious damage underneath all the foliage?
Figure 4: This building receives a Total Confidence Score of Poor due to all the overhang obscuring the view.
I’m sure the model would be very confident looking at this image and concluding there isn’t a tarp, but should we have confidence in it? Looking at the image, I’m confident that there’s not a tarp in that image, but I’m not confident there’s not a tarp on the roof itself. We combine all factors that could lead to uncertainty into a single score: the Total Confidence Score.
The Total Confidence Score consists of a variety of metrics and measurements to ascertain whether or not our results on the image are reflective of the real world. For example, we know that our models’ performances are heavily dependent on the quality of the image, i.e. we’re far more likely to overlook missing shingles when the image quality is poor. So we factor that into our overall Total Confidence Score, along with other relevant attributes.
