I thought Uncommitted was a smart use of the primaries.

More generally, obviously much more critical than in the election itself. But getting the right candidates in the primary, and pushing all candidates to be better in all the usual ways. They’re never going to chase us to the left like they chase to the right, so we have to do the work and set the boundaries.

Choosing to treat anyone who thinks otherwise as too stupid to realise this is exactly why Trump might win. Please stop.

The Democrats do not have a realistic chance of winning against Trump because the Democrats are entirely incapable of challenging power. It’s the fundamental contradiction of liberalism. They won’t do anything for the people they need to vote for them because if they do the people who fund them will stop funding them.

Obama and Sanders both excelled at small-dollar donations, of course. Sadly, Obama was a silver-tongued coward and the Clinton Democrats made sure she didn’t repeat the mistakes of 2008 in 2016 by not bothering to sign up voters in case they killed her in the primaries again.

They dig their own grave and they do so willingly because it makes them exceedingly rich.

You know the article has words in it that aren’t in the headline?

And that if you actually care about Biden winning, you need to engage with these arguments or at least have the good sense to STFU for fear of alienating people even further?

Removed by mod

Learn to read, comprehend what you read, think about what you read, and then avoid saying stuff that gets you the exact opposite of what you want. FFS

Demanding that people vote for the least worst option without any content other than sneering at them for apparently not realising that one of the options is worse, is doing exactly that.

It’s straw-manning the arguments of people who want (and desperately need) the Democrats to be better and are putting serious thought, time and energy into how that is possible in a world controlled by billionaires who unleash fascism the moment their power is threatened.

And they’re doing it with a lazy, cynical, Bill Maher-wannabe take because apparently they think this is a good look?

They’ll be the death of us all.

Yeah, if you’re going to comment you would, ideally:

1. read the article

2. comprehend what it is saying

3. respond to it

Knee-jerk hand-waving is not useful. It’s worse than just a waste of your time and ours, you are actively alienating everyone you desperately need to hold their nose and vote for Biden.

What are you trying to achieve here?

Obediently voting for the least worst option means you eventually run out of good options. <- we are here

The conundrum is working out how you force those options to get better without accelerationists getting to test out their theories for real (again).

I would respectifully suggest that “shut the fuck up and vote” does not cut it.

They’ve been working on this for years, an opportunity to schism gracefully was offered in advance. In the UK, they’ve allowed individual congregations to decide what can happen in their church building(s), and individual clergy to opt out of officiating.

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.

How is the microphone for phone calls?