I was known to yank a power chord or two back in college…

The italics are a nice hint. Good Poe’s Law submission.

I got that advice as well – the explanation given to me was that it’s almost always used incorrectly, so just be safe and don’t. However, I like the way it makes writing more closely resemble natural speech; we usually talk in conjoined clauses rather than complete sentences.

I’ve been trying to get the semicolon to take off; haven’t had much luck though.

But following the surface of a sphere causes you to constantly change direction

I think you’ve generalized a bit too much. The gender-swapped format was not created for this exact meme, it’s just a jovial commentary on male stereotypes women find unappealing. In this case, the joke isn’t that she saw a stack of random games, they’re all FIFA – did you maybe not notice that? The annoying male FIFA player is a pretty well-established meme at this point.

Uppies for all of you!

Put your foot down everywhere then – it’s a fallacy to think that it’s not worth it to resist data harvesting because it already gets collected “everywhere” anyway, take one step at a time to make it harder and harder. Opting out of this is just one step.

Isn’t reducing the size of the dataset worth it? I’d rather them have a picture from three years ago than a new scan every month or two.

It’s not such a binary thing as winning or losing, it’s a constantly shifting process. The only way to actually lose is by giving up – instead, consider it making it as hard as possible for your privacy to be infringed upon. Sometimes it’s more inconvenient, but what makes us such a farmable populace is our reluctance to be inconvenienced. Be good at being uncomfortable.

I refused, it went fine. I had to repeat myself because it was unexpected and dudebro wasn’t prepared, and they had to turn on the other machine and wait for it to start up, but it only delayed me like 2 minutes. The more people ask, the easier it gets.

Mine is sate vs satiate

It’s the NYT Mini

I fucking love beans

What would be extremely rock and roll-- punk rock, even – is donating all of the proceeds from that show to pro-union efforts.

#DonateItDave, or something

I dug in (thanks for linking sources) and there are some promising details. The ~80% figure for the US is from a 2011 report (even though the citation states 2014…), so it’s very old. In 2019, the US began an initiative to increase awareness of this issue and address it, see the progress here (pdf link).

Not trying to counter the narrative, but at least we’re talking about it on the federal level, so maybe that can provide some optimism to people.

The mishandling is indeed what I’m concerned about most. I now understand far better where you’re coming from, sincere thanks for taking the time to explain. Cheers

Thanks for the response! It sounds like you had access to a higher quality system than the worst, to be sure. Based on your comments I feel that you’re projecting the confidence in that system onto the broader topic of facial recognition in general; you’re looking at a good example and people here are (perhaps cynically) pointing at the worst ones. Can you offer any perspective from your career experience that might bridge the gap? Why shouldn’t we treat all facial recognition implementations as unacceptable if only the best – and presumably most expensive – ones are?

A rhetorical question aside from that: is determining one’s identity an application where anything below the unachievable success rate of 100% is acceptable?

Can you please start linking studies? I think that might actually turn the conversation in your favor. I found a NIST study (pdf link), on page 32, in the discussion portion of 4.2 “False match rates under demographic pairing”:

The results above show that false match rates for imposter pairings in likely real-world scenarios are much higher than those from measured when imposters are paired with zero-effort.

This seems to say that the false match rate gets higher and higher as the subjects are more demographically similar; the highest error rate on the heat map below that is roughly 0.02.

Something else no one here has talked about yet – no one is actively trying to get identified as someone else by facial recognition algorithms yet. This study was done on public mugshots, so no effort to fool the algorithm, and the error rates between similar demographics is atrocious.

And my opinion: Entities using facial recognition are going to choose the lowest bidder for their system unless there’s a higher security need than, say, a grocery store. So, we have to look at the weakest performing algorithms.

Yeah I got the impression it was a recoverable condition after a search found a bunch of guides for “unbricking” (Android phones). Semantics are the true enemy it seems