You think there’s going to be civil war and also, you want to maximise the numbers fighting for the fascists. Cool, cool.

Because handing election victories to fascists is a really, really bad idea.

Because there is no mirror image.

@pjwestin@lemmy.world has given you a good description of fascist methods. They’re not available to the opponents of fascism because they are not fascists.

Fascism appeals to the worst parts of our nature. It gives permission to those feeling fear, humiliation or shame to lash out in anger and destroy the people that make them feel that way.

You can’t deploy the same tactics to make those people want to be on your side instead. If you try to shame them, they will just hate harder.

You should, of course, expose and ridicule the grifters who lead fascist movements and punching fascists is encouraged. But you need to distinguish between authoritarian leaders and the people they seek to lead.

You should not pander to the billionaire-funded leaderships (take note NYT), but you must not sneer at the people they are trying to lead (take note centrist Dems).

Advising a parent to torture a child over food is piss poor advice to start with but when the parent has identified possible autism, you realise you know less than nothing and shut the fuck up.

So the fuck what?

What did you think this bit meant?

(He’s likely on the spectrum.)

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?

Yes. I never said any different. It was adopted as a descriptor by gay men, not bigots trying to denigrate them.

These two examples are quite different, I think.

Gay was not originally a slur, AFAIK. It was adopted as a less clinical descriptor by gay people, especially gay men (again, AFAIK). There have been concerted efforts to make it into a slur and it is often used in a derogatory fashion, but it does not have a pre-history of being used as a slur.

Queer is the opposite. It was used as a slur and it is a rare example of successful reclamation of a word. A slogan in the 1980s on Gay Pride protests was “We’re here, we’re queer, we’re fabulous, get used to it”. At the time, queer was very much a slur so the chant had a bite that you wouldn’t hear in it today.

You’re nitpicking the headline while agreeing with the article.

“What is striking is that the uncool, mean standards of FOSS conduct that many of us have decried for years, and that many defended as authentic, tough, etc., ended up not just being exclusionary loser behavior, but a significant attack surface.”

That’s a problem for people who use Meta. How is it a problem for people on Mastodon?