I fully agree that errors in extracted data can lead to making incorrect decisions/policies. Even for applications where accuracy is paramount, though, I think error-prone models still have their uses:
- For applications that only need summary statistics over certain geographies, analyzing small samples of data can yield correction factors and error estimates.
- The data could also be combined with manual verification to improve existing higher-precision but lower-recall datasets (e.g. OpenStreetMap where features are more likely to be correct but also have less overall coverage).
- For applications that only need summary statistics over certain geographies, analyzing small samples of data can yield correction factors and error estimates.
- The data could also be combined with manual verification to improve existing higher-precision but lower-recall datasets (e.g. OpenStreetMap where features are more likely to be correct but also have less overall coverage).