Earlier quoted context omitted.
> It depends - These models are often non-transparent in terms of how they calculate, so what if the AI/model decides/calculate that a higher percentage of minorities in an area is a high predictor of crime, thus indirectly creates a policy that, for instance, areas with high levels of ethnic minorities require higher levels of policing or harsher restrictions (i.e. curfews, more CCTV e.t.c.). It's pretty simple to m…
> It's pretty simple to make the models transparent. Just select the inputs you want the model to have access to. You shouldn't be feeding your model the percentage of minorities in an area. That doesn't make the model transparent, it just makes the input and output transparent, but it's still a black box. It's nice to say that everyone will ensure that the model isn't fed with information which will allow it to make…
> accidental racial bias has already shown up multiple times in applications where it should be easier to detect than an invisible 'crime prediction' algorithm that the general public won't have access to but public policy will be implemented on the basis of.
The primary victims of violence are often of this same race as the "accidental racial bias" you're talking about. You're not doing anyone any favors by misallocating police resource to be more "equitable" without considering actual need. Only 7% of Americans want less policing in their neighborhoods. I imagine people that live in high crime areas would prefer more policing.
https://thehill.com/hilltv/what-americas-thinking/562738-pol...