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GcL
GcL
7:10 AM
@AncientSwordRage Ah. Gotcha. That's the implicit cognition. However, if you've got a model and you can't figure out ways to get it to "pump the wrong direction" or emit a bad outcome, then you're looking at something that's non-falsifiable, and that's not science.
I think a more tractable example would be something like, "in physics, can you have a perpetual motion machine?" And with the current model, the answer is "yes, but it would require negative mass."
That kind of assumption, "assume negative mass exists", is something that would get noticed (and did for a number of "warp drive" papers a few years back) and flagged for the proposed model.
Similarly, in social science or community health models, one could choose predicates, tunings of parameters, or training data that emits a result affirming some outcome of systemic racism. I would expect few would bother to go through the exercise, and if it were done, the assumptions needed to get such a result get flagged.
A far more prevalent problem currently is observation sets that have baked in racist or systemic racism effects that are then used as training data for models. E.g. risk assessment for mortgages and loans or one that is a terrible problem here is auto and home insurance rates. The actuarial models based on historic data has the problem of being terrible skewed in this way.
The solution is to model more equitable distributions of risk, but that requires generating synthetic observation sets based on "what would it look like if". There's a decent amount of nuance and debate about how to choose models such that they are trying to predict a fairer world than we exist in.
 
 
7 hours later…
AncientSwordRage
AncientSwordRage
1:57 PM
@GcL I think the fundamental issue with our conversation (leaving anything said by us or anybody else in the main chat aside) is that you seem set on viewing any argument from within science (by which I mean 100% predicated on empirical evidence and logic), and my whole argument is that some decisions need to be made (even if only partly) outside of science (i.e. using something that does not rely on empirical evidence). As a different example, say a person knows they will give birth to a child with a congenital condition. You can look at data collected, look at surveys, build decision tree…
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