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Google offers to help others with the tricky ethics of AI

arstechnica.com

21–30 of 35 posts

Re: Google offers to help others with the tricky ethics of AI

#21
> Longer term, the company may offer to audit customers’ AI systems for ethical integrity, and charge for ethics advice.

If Google'll act as any other company in a free market, then this'd seem like a neat offering. Maybe it'll be a helpful service, or maybe it'll be a flop; but, worst-case scenario, just don't use their service if it's no good.

But the language of "auditing" and "charg[ing] for ethics advice" sounds almost like someone's laying the groundwork for regulatory authority over AI.

Re: Google offers to help others with the tricky ethics of AI

#22
post #19

A lot of knee-jerk Google-hating in this thread, which I think is unfounded in this specific context. Google’s AI safety and AI bias toolchain is by far the most robust that I’ve seen. Seems to me that they are investing much more heavily than other players. Note that this is a different issue than data privacy, which Google rightly takes flak on. AI bias is referring to questions like “if I train a network to (sente…

If Google were serious about safety, they wouldn't be after medical data. Instead, they would offer their software to universities and hospitals and let them run their software on their data.

Re: Google offers to help others with the tricky ethics of AI

#23
post #22
post #19

A lot of knee-jerk Google-hating in this thread, which I think is unfounded in this specific context. Google’s AI safety and AI bias toolchain is by far the most robust that I’ve seen. Seems to me that they are investing much more heavily than other players. Note that this is a different issue than data privacy, which Google rightly takes flak on. AI bias is referring to questions like “if I train a network to (sente…

If Google were serious about safety, they wouldn't be after medical data. Instead, they would offer their software to universities and hospitals and let them run their software on their data.

How do you build the software without the data? It seems that, even if the eventual goal is entirely on-premises computation, there still needs to be a period where Google has access to the data.

One might suggest that Google could create partnerships with medical facilities in order to do the development. Of course, as soon as the news of any partnership with Google arrives, there's an immediate outcry over the data privacy issues. It seems impossible for them to act in this space without receiving flak.

I believe there's also the risk that, since they're going to be blamed for all possible evils whether they do them or not, they may as well go ahead and reap the rewards of such bad behavior.

Re: Google offers to help others with the tricky ethics of AI

#24
post #23
post #22

Earlier quoted context omitted.

If Google were serious about safety, they wouldn't be after medical data. Instead, they would offer their software to universities and hospitals and let them run their software on their data.

How do you build the software without the data? It seems that, even if the eventual goal is entirely on-premises computation, there still needs to be a period where Google has access to the data. One might suggest that Google could create partnerships with medical facilities in order to do the development. Of course, as soon as the news of any partnership with Google arrives, there's an immediate outcry over the data…

> since they're going to be blamed for all possible evils whether they do them or not, they may as well go ahead and reap the rewards of such bad behavior.

This doesn't seem to imply that Google is serious about security which was the original point.

Re: Google offers to help others with the tricky ethics of AI

#26

Earlier quoted context omitted.

No they are the big scapegoat. Seriously it is a zeitgeisted Big Lie where they streneously insist it is a problem and make impossible ignorant standards while failing to provide evidence just fear mongered bullshit from the losers. The actual problems with AI ethics are down to "it isn't ready for that morons" and "It would be obviously abusable even if it was jackass."

There are real problems deploying biased systems. Facial recognition is a big issue. That’s not a problem of users, but of the creators.

Facial recognition will be a far bigger problem when it works perfectly.

Re: Google offers to help others with the tricky ethics of AI

#27
post #19

A lot of knee-jerk Google-hating in this thread, which I think is unfounded in this specific context. Google’s AI safety and AI bias toolchain is by far the most robust that I’ve seen. Seems to me that they are investing much more heavily than other players. Note that this is a different issue than data privacy, which Google rightly takes flak on. AI bias is referring to questions like “if I train a network to (sente…

That example (https://research.google.com/bigpicture/attacking-discriminat...) isn't particularly compelling.

As their example shows, there're two traditional approaches: maximize profit and group-unaware.

* Maximizing profits gets the most profit, but treats people differently based on their group.

* Group-unaware treats everyone the same regardless of their group, but can generate far less profit.

The example presents two alternatives: "demographic parity" and "equal opportunity". Presumably the authors would argue that these may be superior choices because they generate nearly as much profit as profit-maximization while having a plausible argument for being socially responsible.

This seems a bit off.

Fundamentally, we might say that there're 2 kinds of discrimination: fair and unfair. For example, it might be fair to discriminate for objective reasons, but it'd be unfair to discriminate for non-objective reasons (e.g., bigotry).

The advantage of profit-maximization is that it takes full advantage of fair-discrimination while fully avoiding unfair-discrimination; the drawback is that it does discriminate.

The advantage of group-unaware is that it fully avoids all discrimination; the drawback is that it sacrifices fair-discrimination, causing it to yield the lowest profits.

The two alternatives proposed in that example seem to get the best of both worlds because they're basically just cloning profit-maximization, but with slight concessions to plausible-sounding criteria for equality to dodge perceptions of unfair-discrimination.

Here're the tricks:

* In "demographic parity", everyone has the same odds regardless of group. This would appear to be the same thing as profit-maximization if the risk/rewards were the same, but since it ignores them, it ends up being basically "profit maximization, but ignoring different risk/rewards".

* In "equal opportunity", both groups get the same true-positive rate. This again seems to sacrifice some of the risk-vs.-reward information, but with a slightly different skew.

So for the privileged group (Orange) vs. the disadvantaged group (Blue):

* Profit maximization: $32,400 from 50 vs. 61

* Group-unaware: $25,600 from 55 vs. 55

* Demographic parity: $30,800 from 52 vs. 60

* Equal opportunity: $30,400 from 53 vs. 59

To me, that looks like 3 ways to discriminate, all yielding roughly the same profit and thresholds -- using either of the 2 proposed alternatives gives up a little bit of the profit in exchange for a pleasant-sounding rationale.

What I dislike about this is that it seems entirely superficial. The proposed alternatives engage in roughly the same level of fair-discrimination (and none of them engage in unfair-discrimination, which wasn't given in the example at all) to generate roughly the same level of profit.

---

> The conversation on this subject by lay folks is rife with statistical ignorance, and Google has done good work communicating and clarifying the conversational starting points. This is a hard issue because it takes ethical trade-offs and forces you to specify mathematically exactly how you want to handle inequality, which is a subject that most people haven’t thought through rigorously, and would rather hand-wave away with virtuous sound bites.

That's exactly what this looks like!

In this case, the virtuous sound-bites are "Demographic Parity" and "Equal Opportunity". They both worked out to be mostly the same as simple profit-maximization, but if someone in a disadvantaged group protests that they're being discriminated against, they'd probably find it difficult to follow the math far enough to sustain their complaint.

Re: Google offers to help others with the tricky ethics of AI

#29
post #26

Earlier quoted context omitted.

There are real problems deploying biased systems. Facial recognition is a big issue. That’s not a problem of users, but of the creators.

Facial recognition will be a far bigger problem when it works perfectly.

Sure but biased AI disproportionately affects people of color which is very bad already.

Re: Google offers to help others with the tricky ethics of AI

#30
post #27
post #19

A lot of knee-jerk Google-hating in this thread, which I think is unfounded in this specific context. Google’s AI safety and AI bias toolchain is by far the most robust that I’ve seen. Seems to me that they are investing much more heavily than other players. Note that this is a different issue than data privacy, which Google rightly takes flak on. AI bias is referring to questions like “if I train a network to (sente…

That example ( https://research.google.com/bigpicture/attacking-discriminat... ) isn't particularly compelling. As their example shows, there're two traditional approaches: maximize profit and group-unaware. * Maximizing profits gets the most profit, but treats people differently based on their group. * Group-unaware treats everyone the same regardless of their group, but can generate far less profit. The example pre…

I appreciate the detailed object-level analysis of that case, but my point was more intended at the meta-level — in order to have a conversation about AI bias, we need language and examples to start from. Google is building these fairness metrics into parts of its cloud ML toolchain, and is investing in peer-reviewed research in this area. That’s more than most other companies, and should be applauded.

At the object level, I think you perhaps oversimplify with “ Fundamentally, we might say that there're 2 kinds of discrimination: fair and unfair.” What we think is fair is the crux of the whole issue, and there is not agreement on that distinction. For example, if we price risk at the “true” risk of default, and this means that fewer black people get loans, is that fair? Some think it is, and some think it is not, even though I hope most would agree there is no racist intent in the decision itself. The economic context in which these decisions are made already contains the impact of past racism, which can be perpetuated or or restituted by decisions made now.

I also disagree with your characterization of these types of parity as sound-bytes. My definition of a sound-byte is a conceptually thin phrase that sounds good, but does not contain much content. These are the opposite; they are specific jargon with a precise meaning that is explained in detail in the article. You could call them “Foo” and “Bar” and their usefulness in the conversation would be the same. (Sure, “equal opportunity” has been deployed as a sound-byte elsewhere without any rigorous definition, but I hope you’d agree that this article at least fleshes out one possible concrete definition, even if you don’t think it is the right one. I’m sure they are others - and now we can further the field by writing them down, citing this piece, and making an objective critique!)

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