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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 mos

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 (sentence criminals, price insurance, ...), how do I detect if the resulting predictions are racially biased?” 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. For example, see http://research.google.com/bigpicture/attacking-discrimination-in-ml/ http://research.google.com/bigpicture/attacking-discriminati...

amelius 6y ago
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.
- tanaros 6y ago
  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.
  - amelius 6y ago
    > 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.
_Nat_ 6y ago
That example (https://research.google.com/bigpicture/attacking-discrimination-in-ml/ 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.
Jimmc414 6y ago
The issue is that Google has lost most of it credibility as an authority on ethics. Think about what would prompt one to add "don't be evil" to their company charter, then think about what would prompt one to remove it.

amelius 6y ago

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.

tanaros 6y ago
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.
- amelius 6y ago
  > 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.

_Nat_ 6y ago

That example (https://research.google.com/bigpicture/attacking-discrimination-in-ml/ 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.

Jimmc414 6y ago

The issue is that Google has lost most of it credibility as an authority on ethics. Think about what would prompt one to add "don't be evil" to their company charter, then think about what would prompt one to remove it.