User story summary: as a person looking for a job or for rental housing, I want to receive ads for all jobs and housing units for which I am a qualified employee or tenant, even though I am a member of a group of people against which some employers or landlords discriminate.

A user who is concerned about leaking sensitive information can already choose not to share specific topics. However, topics are being fed into a machine learning (ML) system, which then infers a variety of data points about the user. The absence of a common topic may be a stronger signal than its presence for inferring some data points. (Some examples might include a religious group that avoids certain foods or beverages, or a person with a disability that limits their ability to do certain activities.)

A user can already control the set of topics being passed. In addition, the user needs enough information to be able to meaningfully decide what topics to share, including any inferred data points that are applied to them based on presence or absence of topics. (Simply blocking Topics API entirely is probably inadequate, since ML systems will "learn" to avoid users with Topics API turned off when placing some or all employment or housing ads.)

Open to ideas for possible implementations for how to pass this information back to the user from the caller.