One of the things I allude to—and we talk about this in the report—is the issue of filtration, whereby you can mandate that companies filter the kind of data they use to train their systems. It's been reported in different papers that there can be a filtration tax. That is, if you filter data before you apply it to a model, the model might not perform as optimally as if you gave it unfiltered data, because the more data a model has, typically, the better it can perform on certain tasks.
Filtration can be an avenue to do that, but it also might present some trade-offs for businesses.