A more realistic scenario is "why is my insurance more expensive than the new lower rate that everyone else is now getting? I used to pay the same amount as eve

A more realistic scenario is "why is my insurance more expensive than the new lower rate that everyone else is now getting? I used to pay the same amount as everybody else." If the models improve, you get lower costs provided there is robust competition in the industry. That's why people let insurers drive in their cars now with them via an app to get lower rates. That said - the main issue that creates the Kafkaesque world you speak of is poor correlation analysis. In the short term the insurer doesn't suffer due to false positives. Over time, however, to grow their market base you would expect that they start to test whether their analysis is actually reducing risk. With ML this presents a problem because they may not actually know why profiles are high at some point. Denying financing is different than not lowering insurance rates, to be sure, and would also be looked at differently. I'm having trouble deciding where the slope starts getting slippery here as I hate the idea of some bad week of lawn care causing financial stresses for so many reasons - but insurers also already do all of this with a smaller dataset. I wouldn't assume net rate increases - but I'm not sure if that should impact our overall thinking on the problem. EDIT: > The paper abstract itself calls out where it gets slippery: "From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data."