21–30 of 180 posts
Hopefully anything linked to gender is looked into for example car insurance? Men typically signifigantly more than women for car insurance.
Car insurers can point at actuarial data to back up their rating decisions.
I would take DHH's interpretation of this with a large grain of salt. He seems to assume that living in a "community property state" will be taken into account (presumably then considering his wife to have parity with his income/score) by the credit limit algorithm, but we have no way to know that that's the case. He also adds "It gets even worse. Even when she pays off her ridiculously low limit in full, the card wo…
Not intentionally, but it's possible for black box AI algorithms to engineer such a model feature.
Other than him being very outraged and claiming gender-based discrimination again and again, is there actually anything to suggest the algorithm is biased on the gender axis, as opposed to any other axis? Just to be clear: I am not dismissing the possibility that the algorithm (meaning the training data, really) is gender biased, it just isn't clear from what I have seen in the tweet storm that this is necessarily th…
Other than him being very outraged and claiming gender-based discrimination again and again, is there actually anything to suggest the algorithm is biased on the gender axis, as opposed to any other axis? Just to be clear: I am not dismissing the possibility that the algorithm (meaning the training data, really) is gender biased, it just isn't clear from what I have seen in the tweet storm that this is necessarily th…
> is there actually anything to suggest the algorithm is biased on the gender axis, as opposed to any other axis? None of us are allowed to know that. That's the big problem here. They delegate the decision to a black box that cannot be questioned. When the process is set up like that, I think it's fair to assume the worst case scenarios and put the burden on the company to prove otherwise. Adverse inference is a sen…
The entire article is about questioning the black box, with regulatory force. Which is a good thing that is is going to be investigated.
>When the process is set up like that, I think it's fair to assume the worst case scenarios
Disagree. Consider it yes, but not assume it as a foregone conclusion.
> and put the burden on the company to prove otherwise.
Agree.
I would take DHH's interpretation of this with a large grain of salt. He seems to assume that living in a "community property state" will be taken into account (presumably then considering his wife to have parity with his income/score) by the credit limit algorithm, but we have no way to know that that's the case. He also adds "It gets even worse. Even when she pays off her ridiculously low limit in full, the card wo…
The whole idea behind decisions like these is that they have to be explainable and ML especially does not lend itself well for that.
I would take DHH's interpretation of this with a large grain of salt. He seems to assume that living in a "community property state" will be taken into account (presumably then considering his wife to have parity with his income/score) by the credit limit algorithm, but we have no way to know that that's the case. He also adds "It gets even worse. Even when she pays off her ridiculously low limit in full, the card wo…
Let's assume that there is some valid reason for the difference in credit limits. (I'm not assuming one way or the other, but let's just grant it here.) This is still bad for Apple and for consumers. The "ALGORITHM" that can't be questioned, inspected, explained, or overruled is a massive failure. Whether its criminal justice, credit scores, or behavioral predictions, ceding authority to some "AI" overlord can't end…
Other than him being very outraged and claiming gender-based discrimination again and again, is there actually anything to suggest the algorithm is biased on the gender axis, as opposed to any other axis? Just to be clear: I am not dismissing the possibility that the algorithm (meaning the training data, really) is gender biased, it just isn't clear from what I have seen in the tweet storm that this is necessarily th…
The axis doesn’t really matter. Maybe it’s gender, maybe it’s not. In any case, it should be about money, credit score, trustworthiness. Apparently, it’s not. Being a women could be one indicator. It might be "being black" for other people.
There seems to be something fishy going on and Apple doesn’t even know why. Hence, there is a bias.