I wonder how better, big data being overlaid on virtual reality may be helpful to the currently marginalized in the future? Would it be useful to have shared data about businesses and practices that tend to marginalize people further? I recall an African-American comedian recently talking about the Confederate Flag in a (Netflix?) comedy special. They indicated that the flag actually had some worthwhile uses and reminisced driving on rural highways at night looking for a place to stay. When they saw that flag flying over a motel, they knew better to keep driving and stay at another hotel further down the road. In this case, the flag over the hotel not-so-subtly annotated the establishment itself.

I perceive a lot of social slights and institutionalized racism as being of a marginal sort which are designed to be bothersome to some while going wholly unnoticed by others. What if it were possible to aggregate the data on a broader basis to bring these sorts of marginal harms to the forefront for society to see them? As an example, consider big companies doing marginal harms to a community's environment over time, but going generally unnoticed until the company has long since divested and/or disappeared. It's hard to sue them for damages decades later, but if one could aggregate the bigger harms upfront and show those annotated/aggregated data up front, then they could be stopped before they got started.

As a more concrete example, the Trump Management Corporation was hit with a consent decree in the early 1970's for prejudicial practices against people of color including evidence that was subpoenaed showing that applications for people of color were annotated with a big "C" on them. Now consider if all individuals who had made those applications had shared some of their basic data into a pool that could have been accessed and analyzed by future applicants, then perhaps the Trumps would have been caught far earlier. Individuals couldn't easily prove discrimination because of the marginal nature of the discrimination, but data in aggregate could have potentially saved the bigger group.