Many types of data are prohibited for various types of insurance decisions. This varies greatly, from general rules such as banning price discrimination due to

Many types of data are prohibited for various types of insurance decisions. This varies greatly, from general rules such as banning price discrimination due to race or sex, to specific topics like the ACA[1]'s prohibition against denying coverage due to "pre-existing conditions". With modern data analysis methods, it is (sometimes) possible to infer prohibited data from other collections of data. For example, various machine learning techniques used on economic values such as income, home value, and proximity to good schools might reveal something about recidivism rates, but it's probably finding a confounding variable such as redlining[2] or other types of institutional bias[3]. This is a problem in both legal decisions and insurance, and transparency in how decisions are made is vital in both. That said, the impact on insurance is probably[4] a relatively minor change in price, which is very different from a Boolean decision about someone's freedom. Even in the unlikely case where the statistical biases are known and accounted for, it still isn't appropriate to over-interpret results about populations as if they apply to any particular individual. [1] Affordable Care Act ("Obamacare") [2] https://en.wikipedia.org/wiki/Redlining https://en.wikipedia.org/wiki/Redlining [3] https://www.youtube.com/watch?v=qXQA6D4JC0A https://www.youtube.com/watch?v=qXQA6D4JC0A [4] for definitions of "probable" somewhere between "an educated guess" and "meh; whatever"