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I think the tradeoff is that at least the AI discrimination is systemized, and there's one place you can manipulate to reduce that discrimination, while with pr
by neilparikh 6y ago
I think the tradeoff is that at least the AI discrimination is systemized, and there's one place you can manipulate to reduce that discrimination, while with pre-AI human discrimination, it's not at one place, so it's harder to eliminate.
As an example, it's the difference between being rejected by a central agent for a loan, versus going to your local branch, and being rejected by a random employee at the local branch. It's obviously much easier to change the central agent than it is to change every distributed employee.
Now, whether this is actually the case in practice, and whether this is a good or bad thing is open to interpretation.
- joe_the_user 6y ago"I think the tradeoff is that at least the AI discrimination is systemized" What does systematized mean in this context? The specific problem is that modern deep learning systems are unsystematic - they heuristically determine a result-procedure based on some goodness measure and this result-procedure is a black box. You already have criteria-based algorithms for things like loans - the individual employees aren't making arbitrary decisions or just pen-and-paper calculations. You have a central algorithm now in a given bank, one that can be looked and understood. The question is whether to go from that to an opaque, "trained" algorithm whose criteria can't analyzed directly.
- extropy 6y agoAs far as I can tell the law does not prohibit algorithm assisted decision making. So as long there is a human rendering the final decision we are good. Which seems to be a reasonable balance IMO.
- iR5ugfXGAE 6y ago*It's obviously much easier to change the random employee than it is to change the central agent.