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Stanford apologizes after vaccine allocation leaves out medical residents

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Re: Stanford apologizes after vaccine allocation leaves out medical residents

#231
post #4

Why is Stanford in a position to choose allocations in the first place? I thought the government was going to handle allocations.

The federal government has allocated to states, and pretty much left it up to the states how to allocate internally. With some vague sort of guidelines. Most states seem to be deciding to prioritize healthcare workers (especially those taking care of covid patients) either entirely or among other groups. So the way you do that is delivering to hospitals makes sense. CA presumably allocated a certain amount to Stanfor…

The CDC is creating literature assuming "Promoting Justice" and racial criteria are triage factors for vaccine distribution, sufficient to prioritize essential workers (listed examples are mostly government employees) over at-risk and elderly populations. Even though they admit their own models show that would result in slightly more deaths. With that in mind, perhaps decentralization is preferable.

https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-...

https://www.cdc.gov/mmwr/volumes/69/wr/mm6947e3.htm

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#232

> "[A]lgorithms are made by people and the results ... were reviewed multiple times by people," You got that right. The way they talk about "an algorithm did it" makes it seem as as if they think that somehow explains it, like there was only one algorithm possible handed down from god or something. We'll be seeing more and more of this of course. "We can't be blamed, it was an algorithm! We can't be blamed for trusti…

Also “model” is a synonym for “guess”

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#233

Earlier quoted context omitted.

I really don't think you can call that an algorithm, it's a set of human defined rules. You could call it a formula, but an algorithm implies a series of steps which are not present here.

Since algorithm is defined as "a process or set of rules to be followed in calculations or other problem-solving operations", how is it not an algorithm?

Going by that definition, the rules here are not defining the steps to be followed to do the calculation. The rules here are defining the answer that they want, not the steps necessary to figure it out.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#234

Earlier quoted context omitted.

Two independent things might best be analogized as A:B as C:B. So also computer programmer.

Except that they're not independent and that looks like a fact which the model discovered.

The model should be able to tell the difference between population demographics in a particular country and a definition. Demographically, they're not independent. Definitionally, they are entirely independent.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#235
post #187

Earlier quoted context omitted.

The danger is in thinking ML can magically see past human biases and/or is not subject to it’s own types of bias.

Seems like an overhyped danger. Does anyone actually think that? You would have to not even know that the purpose of a language model is to model human language. But if that's the level of your understanding, why would you select a language model to use as a career guidance oracle? Such a negligent career advisor would be just as likely to use those children's posters with pictures of female nurses and male doctors.…

> For example, a man who's a homemaker instead of a programmer is a less attractive partner for a woman.

This is much to sweeping a generalization to have a place here. You might say a man who is a homemaker is less attractive than a programmer to you, but don't speak for everybody else.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#236

> "[A]lgorithms are made by people and the results ... were reviewed multiple times by people," You got that right. The way they talk about "an algorithm did it" makes it seem as as if they think that somehow explains it, like there was only one algorithm possible handed down from god or something. We'll be seeing more and more of this of course. "We can't be blamed, it was an algorithm! We can't be blamed for trusti…

I'm not even sure this formula is WRONG. It's just not great for morale. Residents are generally under 34 and in good heath. In the USA, only ~2400 people 34 have died from COVID--and that is mostly people with comorbidites. In comparison about ~250,000 people over 55 have died. You'd obviously have do account for life-years lost, risk of exposure [1], etc. I wouldn't be shocked if giving the vaccine to a 25 year old…

Which makes them more likely to have low level symptoms while simultaneously spreading it further. This policy just doesn't make sense.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#237

Earlier quoted context omitted.

I am not a native speaker and I don't think I understand the quote. What should "money laundering for bias mean"?

Since computers are unbiased, the idea is that machine learning is also unbiased. However, that's not true and, if anything, it actually re-enforces a lot of bias. I've never done any, but my understanding is that machine learning is just correlation. It's good at figuring out "what", but not "why". Consider training an algorithm to recognize horses by feeding it millions of pictures of horses. Eventually, the algori…

The problem is we actually conflate two related, but not identical, concepts when we talk about "bias".

The first is statistical bias - feeding algorithms training data that is somehow unrepresentative of the "real world" (or more specifically, the actual class of data for the intended use case). As an example, applying facial recognition to Caucasian faces when the model was solely trained on Chinese faces, you're going to have a bad time. The problem was that your data was "biased", because you actually wanted a model that recognizes "human faces", but you trained on the biased subset of "Chinese faces".

The second is the ethical/political notion of "bias" against individuals; more concretely, the idea that a society is "just" when people are judged as individuals, and not prejudiced by their gender/skin colour/etc. In this respect, when we say "we should not be biased against men", we really mean that "an individual man should not be treated any differently from a woman, even though men are overwhelmingly perpetrators (and victims) homicide".

The complication is that reality is inherently imbalanced/biased. Society can be chopped up into a lot of sub-views that skew towards particular demographics. Some are relatively harmless. "OnlyFans content creators" aren't 50-50 men-women, and men aren't charging the same as women either. Some are not - "murderers" are mostly men, black men are overrepresented in the "criminal" group, and so on.

This raises some obvious questions:

1) Why is this the case? Is this the result of systemic discrimination? Historical oppression? Innate preference? Cultural pressure to conform?

2) If you can answer (1), how does that influence your view of what a "just" society is? For example, do you consider it to be "unjust" to be wary of men (and men only) to protect yourself from random physical violence when you're out and about?

3) Does everyone share your view on what a "just" society is?

4) How do these answers dictate what you should "do" about it? As a voter? As an ML practitioner? As a CEO?

I'm not going to delve further into these questions, because they cause a lot of contention and deserve more time/consideration than I can justify right now in a HN post.

The main reason I decided to comment is that I've seen too much debate that tries to steamroll people into accepting conclusions without considering or answering these questions. Even worse, some people are actively trying to silence others who simply want to discuss these questions, rather than swallowing their conclusions uncritically.

(This is not levelled at you, by the way, your comment just presented an opportunity to lay out my thoughts.)

I don't think anyone can meaningfully discuss the (ethical) concept of "bias" without first laying out a very comprehensive perspective of "society" that touches on all of these points.

There was a joint paper from Google, Facebook (and possibly others) about 2 years ago that I thought handled this exceptionally well. The authors addressed many of these questions honestly and objectively, and most importantly, acknowledged the potential for disagreement.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#238

> "[A]lgorithms are made by people and the results ... were reviewed multiple times by people," You got that right. The way they talk about "an algorithm did it" makes it seem as as if they think that somehow explains it, like there was only one algorithm possible handed down from god or something. We'll be seeing more and more of this of course. "We can't be blamed, it was an algorithm! We can't be blamed for trusti…

"Machine learning is like money laundering for bias" https://twitter.com/pinboard/status/744595961217835008

In the same vein: “Criticism is prejudice made plausible.” - H.L. Mencken

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#239

Under a capitalist system, resources are allocated to those most willing to pay for it. Under a socialist system, resources are allocated to those with the most political power. Under an anarchist system, resources are allocated to those with the most firepower. The vaccine is being distributed under a socialist system. Nobody should be surprised at the results.

Under a capitalist system, those with the most resources have the most political power, and the most firepower.

Under a socialist system, those with the most political power have the most resources and the most firepower.

Under an anarchist system, those with the most firepower have the most resources and the most political power.

Re: Stanford apologizes after vaccine allocation leaves out medical residents

#240
post #9
post #3

The great thing about an algorithm is that when it gives you results that benefit you, you can accept them and then pass the buck on to it when people get outraged: "According to an email sent by a chief resident to other residents, Stanford's leaders explained that an algorithm was used to assign its first allotment of the vaccine. The algorithm was said to have prioritized those health care workers at highest risk…

The whole mess has demonstrated why ethical AI research is important. It's so easy for people to disregard why and how biases exist in their systems when it doesn't affect them personally.

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