I was thinking of Miliband, really. Got the same treatment as Corbyn but it never reached a crescendo because he caved.
And Labour in general, of course. Since Thatcher, at least, they always end up defaulting to this please-Murdoch-at-any-cost nonsense. Not that they were great before Thatcher (1945-51 excepted) but the media was much less extreme back then.
Not just children. The Tories need a kind of generalised hate to keep them in power. “Look! Over there! The poor people have all your money!”. Not because a plurality of the electorate actually fall for it but because the billionaires who own the media keep the noise deafening to make sure no one pays any attention to their grift. Which means that the Labour party is too spineless to oppose it, keeping turnout nice and low while the Tories chase the fash to the right.
That does rather beg the question of whether boys or girls are encouraged to be loud.
But maybe they’re just picking up on teachers’ biases? Teachers Give Lower Math Scores to Girls
Obviously, people want that (the actual question asked was about an “urgent” need to see a doctor).
But this proposal is just a repeat of one of Blair’s worst policy failures, without acknowledging how or why it failed.
When New Labour introduced the 48 hour target to see a GP, the vast majority of GPs ‘met’ the target by closing down their phonelines as soon as they ran out of appointments. In the process, they turned the 48 hour target into a 24 hour target because otherwise they’d only have been able to open the phoneline every other day.
It was very bad back then. It’s much worse now because the NHS was at least relatively well-funded under Blair.
Not that they’re announcing this because they think the policy will work, obv. Just doing their best to make sure the voters blame everyone but them.
[The link is to a video of an election Question Time audience haranguing Blair about the foolishness of this target.]
The why is a much harder question.
You’re right about it probably being true, this is not the first study to find something similar, there’s two others reported on here: Patients have better outcomes with female surgeons, studies find
It’s interesting that this study looked at the proportion of women on the surgical team (not the composition of the surgical team for any specific operation):
Overall, female surgeons performed 47,874 (6.7%) of the operations. Female anaesthesiologists treated patients in 192,144 (27%) of operations.
Hospitals with teams comprising more than 35% female surgeons and anaesthesiologists had better postoperative outcomes, the study found. Operations in such hospitals were associated with a 3% reduction in the odds of 90-day postoperative major morbidity in patients.
There’s some speculation in that first link about differences in aggression and risk-taking. But, given the relative rarity of female surgeons, it could just be a competency effect. If women are a small minority for reasons not related to competency, and 93.3% of surgeons are men, it suggests that almost half the men are in the job because a more competent women didn’t get it. Groups with more women do better simply because they didn’t discount half the talent pool quite so heavily.
I don’t know which jurisdiction you’re in but, while it isn’t illegal in the UK, you’re absolutely right about it being a bad idea and you are correct about the reason. In the event of a crash, it could count against you (in the UK, at least).
I think you overestimate the amount of ‘thought’ going on here. (ref}
The way he plays with the meaning of words
She (or, if you’re not sure, they).
any kind of bureaucratic or rule-based decision-making
Human-written rules are often flawed, and for similar reasons (the sole human thought process that ‘AI’ is very good at reproducing is system justification). But human-written rules can be written down and they can be interrogated. But Apple landed itself in court because it had no clue how its credit algorithm worked and could not conceive how it could possibly be sexist if the machine didn’t get any gender data to analyse.
Perhaps that is the point.
That is, indeed, the point.
It’s asking why don’t we use it for that purpose, not suggesting that there is anything easy about doing so. I don’t know how you think science works, but it’s not like that.
The data cannot be understood. These models are too large for that.
Apple says it doesn’t understand why its credit card gives lower credit limits to women that men even if they have the same (or better) credit scores, because they don’t use sex as a datapoint. But it’s freaking obvious why, if you have a basic grasp of the social sciences and humanities. Women were not given the legal right to their own bank accounts until the 1970s. After that, banks could be forced to grant them bank accounts but not to extend the same amount of credit. Women earn and spend in ways that are different, on average, to men. So the algorithm does not need to be told that the applicant is a woman, it just identifies them as the sort of person who earns and spends like the class of people with historically lower credit limits.
Apple’s ‘sexist’ credit card investigated by US regulator
Garbage in, garbage out. Society has been garbage for marginalised groups since forever and there’s no way to take that out of the data. Especially not big data. You can try but you just end up playing whackamole with new sources of bias, many of which cannot be measured well, if at all.
It’s how LLMs work.
The systems didn’t do anything they weren’t told to do.
You’re thinking of the kinds of algorithms written by human beings. AI is a black box. No one knows how these models obtain their answers.
Where did you get insurance carriers from?
No idea what your post, before or after edit, is trying to say. But the subject of your quoted sentence is “proponents of AI” not “AI”, and the sentence is about what is enabled by AI systems. Your attempt at pedantry makes no sense.
If you’re suggesting that it is possible to build an AI with none of the biases embedded in the world it learns from, you might want to read that article again because the (obvious) rebuttal is right there.
Isn’t that a continuation of “why the outlier was culled”?
Not sure I follow, but I think the answer is “no”.
If you control for all the causes of a difference, the difference will disappear. Which is fine if you’re looking for causal factors which are not already known to be causal factors, but no good at all if you’re trying to establish whether or not a difference exists.
It’s really quite difficult to ask a coherent question with real-world data from the messy, complicated reality of human beings.
A simple example:
Women are more likely to die from complications after a coronary artery bypass.
But if you include body surface area (a measure of body size) in your model, the difference between men and women disappears.
And if you go the whole hog and measure vein size, the importance of body size disappears too.
And, while we can never do an RCT to prove it, it makes perfect sense that smaller veins would increase the risk for a surgery which involves operating on blood vessels.
None of that means women do not, in fact, have a higher risk of dying after coronary artery bypass surgery. Collect all the data which has ever existed and women will still be more likely to die from the surgery. We have explained the phenomenon and found what is very likely to be the direct cause of higher mortality. Being a woman just makes you more likely to have that risk factor.
It is rare that the answer is as neat and simple as this. It is very easy to ask a different question from the one you thought you were asking (or pretend to be answering one question when you answered another).
You can’t just throw masses of data into a pot and expect sensible answers to come out. This is the key difference between statisticians and data scientists. And, not to throw shade on data scientists, they often end up explaining to the world that oestrogen makes people more likely to die from complications of coronary artery bypass surgery.
That kind of analysis is done all the time. But, even if we can collect all the relevant data (big if), the methods required are difficult to interpret and easy to abuse (we can’t do an RCT of being born female vs male, or black vs white, &c). A good example is the proliferation of analyses claiming that the gender pay gap does not exist (after you’ve ‘controlled’ for all the things that cause the gender pay gap).
It’s not easy to do ‘right’ even when done in good faith.
The article isn’t claiming that it is easy, of course. It’s asking why power is so keen on one type of question and not its inverse. And that is a very good question, albeit one with a very easy answer. Power is not in the business of abolishing itself.
Important to emphasise that this is a real, and distressing condition.
But if it is real there will usually be a plenty of evidence that it is real. Sexsomniacs most often attack the person lying asleep next to them, and they’re horrified and confused when they realise what has happened. The CPS should not be dropping cases simply because the defendant can afford expensive lawyers. And the case where they claimed the victim had sexsomnia is outrageous.
How is the microphone for phone calls?
So, none of you have stipends and it is a group project for class. See my original answer. They’re not going to have the same priorities as you and, if you’re going to work with a group, you need to accept that.
Throwing mountains of documentation at them isn’t going to work. Talk to them and find out what it is they are finding difficult. Break it down into more manageable chunks. Rough-code it and work out the details when they have a big picture to work from. Or whatever it is that makes sense given what you’re doing.
so I brought on some people to split the work.
Grad students are underpaid and overworked. Do they have any reason to care as much about the project as you do?
Indeed. Fascism is power protecting itself. And, even if the specifics vary worldwide, power has needed a great deal of protection since it fucked up and crashed the global economy. Again.