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chishaku

The ACLU is doing a lot of great work to hold government accountable when it comes to facial recognition tech.

https://www.aclu.org/press-releases/aclu-challenges-fbi-face...

https://www.aclu.org/press-releases/aclu-challenges-dhs-face...

Would be great to see Amazon's support.

The ACLU ran an experiment with Rekognition and these are their findings:

"Using Rekognition, we built a face database and search tool using 25,000 publicly available arrest photos. Then we searched that database against public photos of every current member of the House and Senate. We used the default match settings that Amazon sets for Rekognition.

... the software incorrectly matched 28 members of Congress, identifying them as other people who have been arrested for a crime.

... Academic research [0] has also already shown that face recognition is less accurate for darker-skinned faces and women. Our results validate this concern: Nearly 40 percent of Rekognition’s false matches in our test were of people of color, even though they make up only 20 percent of Congress."

https://www.aclu.org/blog/privacy-technology/surveillance-te...

[0]: http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a...

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I tried to recreate this using Rekognition. I used a public mugshot database. Here are my results [0] if you're curious about what these false matches look like.

[0] https://medium.com/ml-everything/how-facial-recognition-work...

Some of those false positives are laughably similar. I have no doubt an LEO would be convinced they have the right suspect.

I actually think it’s probably better than most people’s naked eye recognition in a crowd.

Yeah, seems to me like the real problem is that people actually have a lot of doppelgangers, and applying facial recognition to the entire population, even if just as accurate as a real person is just not a useful thing to do.

If you had accuracy similar to a human, and assuming this is an existing procedure humans do use, then of course it's useful -- you're doing the same task cheaper/faster.

Presumably you'd also use the same techniques as used by humans today to narrow down further, like taking into account location, time, etc of match

Doing the same thing but faster and cheaper doesn't necessarily mean it's better. If there's a high cost to a procedure, then the user would need to show the value of checking a candidate in some other way for it to be worth the effort. Making it easier and faster means you're more likely to check against random innocent individuals.

Fast, cheap, good. You can only ever choose two.

Often, you are lucky to get one.

Seems like the equivalent of p-hacking to me. Sure they could apply complex queries to narrow down the suspects in a fair way, but I don't trust them to do that.

> you're doing the same task cheaper/faster

Move fast and break lives.

Sounds like the prosecutor's fallacy to me unless great care is taken to also consider lots of supplementary information when performing a match. (https://en.wikipedia.org/wiki/Prosecutor's_fallacy)

Recommend reading the part about Sally Clark. The last two sentences are an incredibly short and understated horror story.

"Sally Clark was a practising solicitor before the conviction. After her three-year imprisonment she developed a number of serious psychiatric problems including serious alcohol dependency and died in 2007 from acute alcohol poisoning"

I sorta think about it as "what's the likelihood of someone winning the lotto twice in a year?" Certainly the probability that any specific person wins twice (assuming here these are independent events...) is so ridiculously small. And yet probability at least 1 person somewhere is that lucky is incredibly high!

Now ask if my neighbor looks like any random person on the street, sure, it's small. But that chance we find two people somewhere that look very similar is incredibly high.

I think the interesting question is what is the chance of doppelgangers given population size and genetic similarity of the population.

It's something like the "rare disease" paradox in medical testing and sensitivity and specificity.

If I increase the population I test to include any warm body, eventually I'll end up with more false positives than real positives.

If you are looking for one male suspect and comparing them to all the male faces (let's assume it gets gender correct) in the us, with a 99.9999% (six 9s) accurate algorithm you would get something like:

1 true positive 182,499,818 true negatives 182 false positives

Broad scale facial recognition is just an outright stupid thing to do as a sole measure of identification without the use of other information.

Yes, I think in the modern world with these methods and massive data sets being used in law enforcement, medical sector etc, the people working there should undergo a lot of training about how basic Bayesian inference works etc. We are otherwise in a world of hurt. Healthy people being told they have some really serious rare diseases, maybe even starting treatment, innocent people being prosecuted or killed during raids etc.

Yeah... training almost assuredly won't happen sadly. Especially for cops.

I teach this stuff (biomedical engineering statistics) to undergrads.

The hardest thing for them with probability is false positives. conceptually it does not seem to align well with how the human brain works. I've had students literally get so frustrated the cry because they know what the math says but can't accept that you can test positive for a disease and not have it. They know they are wrong but it's just this weird sticky misconception.

I think you nailed it on the head with, it is 'just not a useful thing to do'.

This seems like a clear example for why facial recognition is a technology that is just not 'solved' yet. The appearance of people's faces, especially from similar ethnic backgrounds, is just too similar for a ML model to parse out with any confidence.

I have noticed this in real life. As I get older, I notice it more and more. I'm sure many of you all have too. There are very distinct patterns, or 'buckets', that human faces tend to fall in. I think our brains tend to naturally categorize them accordingly.

It is probably subconscious. I might not be able to articulate it, or put a definite 'name' on a group. But I know I am constantly seeing patterns of faces in public. People I don't know, and have never met, but they remind me of other random people I have seen in public. Or maybe they remind me of a popular celebrity that everyone knows.

Either way, something goes off in my head. I can't help but think to myself, they must have some sort of similar lineage, or genetic background. I subconsciously categorize them into a bucket with others I've seen.

I imagine this is similar to how Rekognition, and other models work. I thought the blog post from the parent commment @bko, is a fantastic example of this. It is actually amazing, when you think about it, that the ML model can match these faces up as well as it does.

To the naked eye, it is clearly not the same person. Rightly so, considering all the images were in the range of 70-80% confidence. But many are remarkably close. I think this illustrates, the concept I am trying to describe. You can notice it, even with the naked eye.

All of this rambling is to say, I agree with Amazon's moratorium on Rekognition.

As impressive as the technology is, it should probably not be used to try pin-point specific individuals yet, or whatever else folks might be erroneously trying to use it for. If we are to trust facial recognition to identify specific individuals, it should probaly be approaching near 100% confidence, and I imagine that level of confidence is a long ways off.

Yeah but I am not sure it is suggested to be used in any other context than where a human would be comparing faces too. Ie searching for someone in a crowd or cctv, or checking a passport picture against someone at the gate. None of that is burden of proof level of accuracy. But it just allows to better cover a crowd or process more people at the airport.

Denmark has a fairly homogeneous population (discounting the much needed immigration) I swear there are basically 5-8 Danish feature sets that sometimes I amuse myself seeing how they are put together.

This of course means that there are doppelgangers or near doppelgangers all over the place. Even funnier, when i first moved back here from the U.S I was staying at a place in which there was this guy who looked exactly like one of my friends from the U.S, even down to facial expressions and mannerisms (maybe that was because he was always extremely stoned).

So this is a good application of the birthday paradox actually and the fundamental misunderstanding people have of this is already a real problem in law enforcement.

Take DNA testing. We all know that given two samples the odds of a false positive are incredibly low. So DNA testing is a great too for eliminating people who might otherwise be suspects or further evidence against the guilty.

The problem is that given this DNA database, someone decided "let's find our suspect by looking for a match". With 1 in a billion chance of a random match and 100M samples, your chances of getting a false positive are really high.

The problem is that there are instances where a DNA match alone is used to prosecute or even convict people.

In recent years this problem has gotten worse due to the rise of familial DNA matching. Given two samples, we used to only have the ability to say if they were a match or not. Now we can say how much of a match they are. How much of a partial match is enough? What's more, you may be implicated by the stored DNA is relatives.

Facial recognition is far more imprecise than DNA. So yeah I fully expect this to get abused by prosecutors and law enforcement.

It's only not a useful thing to do if you care about innocent people being wrongfully convicted, which is as you look at the US justice system[0] is debatable. From an outside perspective the US justice system seems to care more about the false negative than the false positive rate of conviction.

There is already a whole slew of dubious methods[1] (bite mark analysis, blood splatter analysis, fibre comparison) being used to put people in prison/to death, these things are known to be bullshit, but they are still being used. I really think we as technology-literate people should fight as hard as we can against the introduction of facial recognition in the justice system because once it becomes common place it will be really hard to undo it.

[0]: https://www.innocenceproject.org [1]: https://www.latimes.com/opinion/op-ed/la-oe-humes-forensic-e...

I agree. It's incredible that the mismatches were only between 70-80%.

For comparison, I did another test where I took one of those youtube videos that shows you the same person's face over ten years. I used the original 12 year old boy as the image I'm using to match. Over 1,300+ images, it had > 70% confidence in all but 4 images (2 had big sunglasses, one the guy was in green-face, and the other one was actually his wife). And this is from a single picture that's ten years old.

The first part I looked at an open-source facial recognition model that did considerably worse.

https://medium.com/ml-everything/how-facial-recognition-work...

Really? They all look very different to me.

I see similarities in most, especially if I take into consideration that one picture could be "age progressed" image.

Your remark on the racial bias is what also came to my mind immediately. It would only be fair if the percentage of mugshots that are of people of color in their 25k sample was published.

This isn't about being fair to the algorithm, it's about being fair to the people subject to the algorithm's judgement

Which is a distorted discussion already. Instead of questioning the benefit of the technology for policing on the whole it is already shifted to the issues of bias. In the end the algorithm and the data will of course have bias in some form but that isn't even important at that point.

Yay, we have an algorithm judging people, but is it fair to Canadians? Completely off...

This isn't distorting the discussion at all. From the point of view of the ACLU, new technology should only be adopted by policing if it can be proven to not perpetuate or exacerbate existing problems of bias, which IMO is a totally reasonable position. We don't accept the "move fast, break things" ethos in fields like aviation, for example, and if we only listened to technologists, society would be too cavalier about collateral damage affecting innocent people. One could argue that society is already too cavalier about this.

I disagree because you already anticipate large scale deployment with that argument.

Facial recognition AI products were already being sold to police departments and customs agencies. Large scale deployment is in the process of happening. Maybe not at your specific agency, but the ACLU is a national organization, and in their eyes one additional false positive caused by increased efficiency is one too many.

Does it matter? More than half of the world's population are people of color.

It matters for the ACLU because it's an American organization focusing on American injustices; in the US, 73% of people are white, 12.7 are black, and 17.6% hispanic.

You're using the strawman and whataboutism logical fallacies, but you're not actually making a point.

Your statistics are misleading. You put hispanics in twice, since the non-hispanic white population of the United States is around 60%.

Shouldn't matter. What we really want is a classifier that does not take skin colour (or ethnicity) into consideration as a feature.

I know how ML works but I believe we should demand better from these technologies than the limits of our own biases.

Right now, this machine learning algorithm is apparently about as smart as a bigot arguing "yea but percentages show that crime is in fact higher among blacks!". It mainly shows how systemic the racism is, that a dumb ML algo picks up on it.

This is not solved by showing the bigot less statistics about black crime, but by showing them how to pull their head out of their ass.

We should expect no less from our ML technologies, otherwise you'll keep running behind the facts, always fixing errors after they have been learned and made.

Yeah that is hard and we have no idea how to approach it. But the alternative appears to be writing computer programs with the reasoning skills of a racist cop.

I disagree with this statement,

> However, just from a cursory review of the mugshots, people of color were disproportionately represented in the mugshot database. So it’s not entirely fair to criticize the facial recognition technology for matching more people of color.

No, the point is the disproportionate representation, and it is a fair criticism, because it is a fundamental limiting factor in the use of the technology. You seem to be distinguishing the "technology" from the data source, but that is not possible. The quality of the technology depends on the quality of the data provided to it.

> I believe a curated list of mugshots with certain characteristics would result in a similar representation of mismatches. Nothing I have read about the technology suggests that there are inherit [sic, is inherent] bias.

This bias is in the data, not the algorithm per se. This is quite accepted in many, even most, criticisms of machine learning applications in the social sphere. It comes up a lot for example in NLP models, for example with assumptions on gender for professions.

It is a technology problem because it demands a technological solution, because removing any and all bias by hand-curating datasets is just not a scalable approach.

I feel like we're missing the popular vocabulary to describe some of these recurring problems.

The algorithms reflect and amplify biases in the data. They also go into feedback loops once they affect the reality being represented in data.

Imagine that an algorithm selects individuals at an airport for search. Contraband smugglers are caught. The dataset now includes them, evolving the bias. We have already seen this at play on social media, recommendation engines, fraud detection, etc.

The future is worrying. There are ratchets on technologies such as these. Unless there are extraordinary counter-reactions, they will be "affecting their own datasets" en masse very soon.

The term I've heard for this phenomenon is "mathwashing."

The old chestnut for this is "lies, damned lies, and statistics."

The problem of disproportionate representation is a problem whether you're using technology or not. It's not a new problem or specific to tech. If a large percentage of dangerous interactions a police officer has with an individual fits a certain profile (e.g. the third party was a man), the police officer will likely exhibit a different set of behaviors when dealing with that party. Sometimes it's appropriate, many times it's not. Same is true for judges and others in the system and you'll see stricter sentencing for certain groups. This results can lead to a self-reinforcing cycle of oppression.

The difference is with technology these biases can at least try to be addressed directly and create an objective more fair system.

The major issue is that the tech is not ready. If you reduce the false positive rate by 5% but you facilitate processing 20% more claims (to use some arbitrary numbers) you are still increasing the absolute number of false positives by 14%. With the punitive American justice system, that is 14% more people who will be detained unjustly, and then when they leave they will be denied most opportunities for housing, jobs, and other things needed to participate in society.

Technology can address biases but we need to make sure that they are addressed sufficiently, so that we do not end up causing more problems than we solve.

> If you reduce the false positive rate by 5% but you facilitate processing 20% more claims (to use some arbitrary numbers) you are still increasing the absolute number of false positives by 14%.

This is a really interesting point that I'll keep in the back of my mind, thanks for that! I hadn't even thought it through in such a concrete manner but I think this really hits the nail on the head.

The bearded guy `deb_haaland.jpg` being matched with a non-bearded guy is interesting; shaving would totally fool me, but looking at the match, it definitely looks plausible. Good job, Facial Recognition AI!

Deb Haaland is the lady underneath. The bearded guy is Rick Crawford.

> Playing tricks using statistics is not new. OJ’s defense lawyer argued that despite OJ being an abusive husband, it is statistically unlikely that he killed his wife.

> > Only one in a thousand abusive husbands eventually murder their wives

> The more pertinent question is what percentage of murdered women were murdered by their abusive ex-husband?

Shouldn't this be "what percentage of murdered women with an abusive ex-husband were murdered by said ex-husband"?

Statistics is only capable of predicting patterns across a sample of data. It is pretty inaccurate when you try to use it to predict a single data point. The Lawyer knew that but also knew a ton of people don’t understand stats

As someone who hasn't explored the service, thanks for taking the time to do this.

> Would be great to see Amazon's support.

From one of the links on the left of the article: https://blog.aboutamazon.com/policy/amazon-donates-10-millio...

> Update, June 9: Since announcing our $10 million donation, we’ve heard many employees are making their own contributions—and we’ve decided to match their donations 100% up to $10,000 per employee to these 12 organizations until July 6, 2020.

I was thinking filing amicus briefs in support of the ACLU in the cases mentioned above but this is helpful too.

I'm a big ACLU supporter, but thought this was poorly done. They never released either the database or the code for this testing, and had configured the recognition level against Amazon's recommendations.

> had configured the recognition level against Amazon's recommendations.

Citations?

My understanding was that the ACLU used the default settings.

July 26, 2018 — Amazon states that it guides law enforcement customers to set a threshold of 95% for face recognition. Amazon also notes that, if its face recognition product is used with the default settings, it won’t “identify[] individuals with a reasonable level of certainty.”

July 27, 2018 — Amazon writes that even 95% is an unacceptably low threshold, and states that 99% is the appropriate threshold for law enforcement.

https://www.aclu.org/press-releases/aclu-comment-new-amazon-...

Either way, the defaults are the problem if the application is law enforcement.

"Defaults have such powerful and pervasive effects on consumer behavior that they could be considered “hidden persuaders” in some settings. Ignoring defaults is not a sound option for marketers or consumer policy makers. The authors identify three theoretical causes of default effects—implied endorsement, cognitive biases, and effort..."

https://journals.sagepub.com/doi/10.1509/jppm.10.114

In the ACLU's posts, they say that they used the "default match settings". In their response [0] to Amazon's response, the ACLU links to a guide published by Amazon [1] intended to "Identify Persons of Interest for Law Enforcement" that does use `searchFaceRequest?.faceMatchThreshold = 0.85;` (this is still the case today).

Fast Company [2] writes about this as well: "The ACLU in both tests used an 80% match confidence threshold, which is Amazon’s default setting, but Amazon says it encourages law enforcement to use a 99% threshold for spotting a match." That bit of the article links to the CompareFaces API documentation [3] which (still) states "By default, only faces with a similarity score of greater than or equal to 80% are returned in the response".

Have you seen/read something else about this?

[0] https://www.aclu.org/press-releases/aclu-comment-new-amazon-...

[1] https://aws.amazon.com/blogs/machine-learning/using-amazon-r...

[2] https://www.fastcompany.com/90389905/aclu-amazon-face-recogn...

[3] https://docs.aws.amazon.com/rekognition/latest/dg/API_Compar...

>Fast Company [2] writes about this as well: "The ACLU in both tests used an 80% match confidence threshold, which is Amazon’s default setting, but Amazon says it encourages law enforcement to use a 99% threshold for spotting a match

Then this whole thing is potentially misleading because there's a huge difference between 80% and 99%. It's probably nonlinear and they could possibly see their false matches drop to 0. This is not a fair test - or rather, the conclusions are not quite supported by the parameters.

Not that I'm defending police use of facial recognition tech, I think it's abhorrent, though possibly inevitable.

Just to make sure I understand, which "whole thing" is misleading? The ACLU's test? Amazon's response?

As for the test, you say it's not a fair test. The point / conversation right now seems to be about the choice of parameters used by the ACLU. As far as I see / understand, the ACLU used the default parameters (and/or those recommended in the documentation / articles that are still up today with those same non-99% values).

What would have been a better / fairer test?

What are police departments using? My uninformed guess would be not 99%. I think therein lies the concern...

My cynical guess would be "whatever the lowest number they can get away with using".

I would bet good money that cops KPI goals benefit from false positives, since they'll reward higher "number of identified/interviewed suspects" and "number of arrests" as a positive thing even if "number of convictions" doesn't line up.

Even more cynically, I'd bet this is a powerful technique for ambitious cop promotion, and that there's little blowback on fraudulently manipulating parameters that adversely affect POC much more significantly that white people.

Thinking about it, I'm now recalling the multiple reports of police departments claiming to not be using clearview.ai, only to have to backtrack when clearview's customer data got popped and it became public knowledge that individual cops were signing up for free trials - which their department/management either chose to hide or didn't know about. That's reasonably compelling circumstantial evidence to me that ambitious cops are quick to jump on unproven and unauthorised technology with insufficient or oversight or with management actively avoiding oversight for them...

In regards to the KPIs this is a known reality. Most states get money from the federal gov highway safety program. Then the states disburse it to local police depts, and the expect high numbers of citations (or even warnings) to be reported back up the chain. It is only for DUI that verdicts are considered, and that's only amongst the smarter states. Related to crime, there are NO KPIs based on the final outcome - all on the elements the police are able to carry out and be accountable for on their own. This makes sense in some ways beyond self promotion. I will say also that the general inflation of KPIs in order to justify promotions, grant renewals, etc is RAMPANT in state and local govs, but especially in policing when it comes to new tech investments and promotions

If they can turn the knob, why wouldn’t they? This stuff isn’t admissible in court, and you can sweep for potential matches to follow up on.

If the default is 80, most will be 80. The SE may say “I’m told to inform you that you should use 99.”, but I’m sure he is winking.

Wouldn't it be more likely that they say "ok, we can interview/investigate/whatever X number of people" and then they adjust the threshold to produce that number? If 80% gives them 10,000 hits and 99% gives them one or none, then nobody is going to just go with either setting.

I'd guess with the potato quality of facial pictures from incidents security or phone cameras, you might want lower confidence matches to get outcomes out of lousy pictures.

They made a facial recognition tool available to law enforcement and in the marketing it says "requires no machine learning expertise to use" then I think it's fair to look at any value of the threshold parameter they make available. Especially a parameter that, by changing it, will give you the answer you want more often.

I'm deeply troubled by the text I've seen here implying this threshold is some accuracy percentage or positive predictive value percentage. Unless God is working behind the scenes at AWS they can't make any claim about the accuracy of the model on an as yet unseen population of images.

That's even before getting to the more esoteric map vs territory concerns like identical twins, altered images, adversarial makeup and masks, etc.

My guess is: Amazon writes that 99% is the recommended threshold because there is little chance of false positive. If an agency is deploying this system and complains that it isn’t working well, a solutions architect will say in a meeting (but not in writing) to lower the threshold. If it comes out that false positives occur, AWS isn’t responsible.

True, but one can't blame Amazon in this case. The agency can reduce the threshold to 0% (ad absurdum) and claim that Amazon's technology is not working.

You can blame Amazon if there's no point on the threshold scale where it works adequately.

I guess one must define "adequate" first. The default value of 80% could work fine if you are developing a "find your celebrity doppelgänger" game while law enforcement should probably use 99%.

I think you'd have a point at which the threshold increases but the probability of the true positive being in your results starts to drop severely. 99% might be useless if you have one or two hits and they are unlikely to be correct. You can't assume that the one you're looking for will be a 100% match; if it was, then you'd just set the threshold to that, presto.

It seems realistic other users would ignore Amazon's recommendations for proper configuration.

Specially given that turning the parameter _down_ will give you more matches. This is great for demos. "No match found" is not so great.

I agree. I wish the ACLU would re-run the results at 99%. But Amazon's example post about law enforcement has it set to 85%.

I don't think this 99% thing is communicated properly at Amazon if it's getting through blog posts like this.

So I think a valid criticism is that we need to make sure that it's higher.

https://aws.amazon.com/blogs/machine-learning/using-amazon-r...

Do we know police actually use 99% ?

Nope and I doubt they do. But a test of 99% would better for Amazon/ACLU to take on.

I do not understand, a fair test is to replicate reality. Also I am wondering if you have for each city a different software package with it's own config or IT guy that tweaks the config

I posted in another comment, but I tried to recreate this using default 70% match. The dataset was 440 images of congressmen and 1,756 mugshots. There were ten mismatches between 70-77% certainty

https://medium.com/ml-everything/how-facial-recognition-work...

If so inclined, support the ACLU:

https://action.aclu.org/give/donate-to-aclu

The ACLU used to be on the top of my list of my personal favorite charities, but I've found they've become significantly more reactionary in the last few years, prioritizing emotionally outraging causes over utilitarian causes, so don't donate to them any longer.

With that said, some of their work is still great, and I'm thankful for it.

I'm scared of that outcome. I'm glad you donated to them in the past so you obviously valued them at some point. Do you have articles to back that up? I'm not trolling; I wonder if this is the message that the "powers-that-be" want us to think.

Remember how McDonalds was the victim of a baseless lawsuit? Well, it wasn't actually the case, but that sure benefitted corporations who can now assert most lawsuits against them are frivolous.

https://www.caoc.org/?pg=facts

Having read extensively on that case, my opinion changed. I now consider the lawsuit baseless and the verdict primarily the result of a sympathetic plaintiff and an unsympathetic defendant (the stereotypical sweet old lady vs the stereotypical evil money-grubbing mega-corp).

Their coffee is just as hot nowadays and to lower the temperature to the degree to where the effect on Stella would have been meaningfully different would result in lukewarm, under-extracted coffee that fewer people would be interested in. Further, the sheer quantity of Mcdonalds coffee moved every year without incident implies user error, rather than product error.

Yes, I know what the jury said and how they divided up the blame. I disagree with their conclusion.

> and to lower the temperature to the degree to where the effect on Stella would have been meaningfully different would result in lukewarm, under-extracted coffee that fewer people would be interested in.

Citation needed.

Hot water systems in homes are generally set to not go hotter than 120F, because much hotter than that could burn somebody in seconds. 120F is a bit more than "lukewarm" (which I take to be an exaggeration), but is nevertheless cooler than anybody serves standard coffee at. 140F (60C) water can give you third degree burns in 5 seconds.

(disclaimer: quick back of the envelope research, mistakes might have been made, void where prohibited, kids eat free)

Stella's own doctor testified[1]:

>Lowering the serving temperature to about 160 degrees could make a big difference, because it takes less than three seconds to produce a third-degree burn at 190 degrees, about 12 to 15 seconds at 180 degrees and about 20 seconds at 160 degrees.

The NCA (a coffee industry group) recommends[2] holding at a temperature of 180-185, due to "rapid cooling", and consuming at or below 140.

Stella's injuries were exacerbated by:

* The hot coffee permeating through thin sweatpants and being held against the skin.

* Her age - 81 years old at the time of the injury. Older skin is damaged more easily[3], and would also have implications for her mobility (how fast she could remove the soaked sweatpants)

Some experiments [4] show that coffee served at 180 will cool to around 162 in 5 minutes, 148 within 10, and 138 within 15. 70% of Mcdonalds business is through the drive-through [5], so must customers would be getting their coffee to go.

The question I'm unable to find a satisfactory answer for is how long it takes the average customer to receive their order and return home. I could probably figure that out if I knew how far the average customer was from their store, but that information is not readily available.

If holding at 180 results in optimum drinking temperature of around 130 in about 15 minutes (per 4), then this is the optimal temperature to hold at for product quality if the average customer lives within 15 minutes of a Mcdonalds.

If you were to hold at 160, using the info from [4], the coffee would fall below this optimum temperature in about 10 minutes and require reheating, which alters the flavor.

[1]: https://web.archive.org/web/20150923195353/http://www.busine... (page 4, bottom)

[2]: https://www.ncausa.org/About-Coffee/How-to-Brew-Coffee

[3]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3377829/ (It is widely accepted that elderly burn patients have significantly increased morbidity and mortality. Irrespective of the type of burn injury, the aged population shows slower recoveries and suffers more complications.)

[4]: https://www.ukessays.com/essays/mathematics/equation-to-mode...

[5]: https://www.reuters.com/article/us-mcdonalds-meeting-idUSKBN...

Do people really buy Mcdonald's coffee, drive home, and then drink it? I'd have thought most people consume their drive through stuff while still in their car.

Yeah, I do that all the time. (I mean, I certainly do now, but even before the pandemic I would usually wait to get home before eating my drive-through meals.)

What you say about the coffee temperature is true as far as I've been able to determine, but didn't McDonalds change their cup design in response to the injury? From what I understand, the cups they used at the time were prone to collapsing.

Its true that the McDonald's case is often presented in a one-sided fashion that makes it look like it was a baseless lawsuit.

But its also true that responses, like the one you linked to are also often one-sided. And what would you accept from an association of lawyers who make money launching such suits?

The truth is, its not as simple as either side tries to make it out to be. I think the Wikipedia article: https://en.wikipedia.org/wiki/Liebeck_v._McDonald%27s_Restau..., does a good job of presenting pertinent details from both sides.

Agreed, I had a much different perspective after reading the whole story.

>Ira Glasser says the organisation he once led has retreated from the fight for free speech.

The ACLU would not take the Skokie case today’: https://www.spiked-online.com/2020/02/14/the-aclu-would-not-...

Former ACLU board member Wendy Kaminer:

The ACLU Retreats From Free Expression: https://www.wsj.com/articles/the-aclu-retreats-from-free-exp...

to complaints of sexual violence. We will continue to support survivors.”

The ACLU Declines to Defend Civil Rights: https://www.theatlantic.com/ideas/archive/2018/11/aclu-devos...

From your link:

> McDonald’s operations manual required the franchisee to hold its coffee at 180 to 190 degrees Fahrenheit.

> Coffee at that temperature, if spilled, causes third-degree burns in three to seven seconds.

They might also be interested to know it was composed primarily of dihydrogen monoxide, a lethal chemical agent known to be the proximate cause of hundreds of deaths each year. They are describing boiling water.

Even if McDonald's did the wrong thing, it is a frivolous lawsuit. I serve boiling hot tea to all my guests, it isn't negligence. The case as described seems to be that McDonald's should be civilly liable for serving a hot beverage in a styrofoam cup to a customer who asked for a hot beverage and could easily detect it was in a styrofoam cup.

> I serve boiling hot tea to all my guests

You hand styrofoam cups out your window to guests you know won’t be staying? Curious social habit.

> a customer who asked for a hot beverage

Coffee is brewed at very high temperatures but is rarely - if ever - consumed at those same temperatures.

https://pubmed.ncbi.nlm.nih.gov/18226454/

For most people, getting a cup of coffee that is near boiling would make for a very unpleasant surprise.

I get junk mail from the ACLU, but haven't given anything to them in a few years. They recently sent me an email saying "thanks for your wavering support".

Bringing up Stella Liebeck makes me feel irrationally hostile; I've had a negative reaction to people "busting the myth" of that lawsuit on the internet for probably 20 years.

I certainly believe that suing corporations with deep pockets is a reasonable and moral way to deal with medical bills in a country without universal health care. It's not a good system, but if you can get away with it, why not?

And I know well that on average, the scourge of frivolous lawsuits against corporations is a myth, because I've worked in the legal industry and have a perspective based on many other lawsuits.

And if McDonald's served coffee without a properly secure lid, or some other defect then they should be held responsible for every penny of damages.

However, I am irritated by anyone who may insist that it is a "fact" that serving coffee which is hot (but less than 212 deg) is negligent in itself. And if I continue to see this "myth busted" or "fact checked" for the next 20 years, it's not going to change my opinion, because assuming I live that long, I'm going to be boiling water for coffee on my stove almost every day.

That tea was hot enough to cause bad burns, and did cause bad burns. If you wanna fact check it, heat 12oz of water to 185F and dump it in your lap.

It was known by management to be dangerous; that was done deliberately so that people would be forced to sip slowly, to discourage refills. Ain't negligence in light of intent

When I make coffee it is also right around 180-185F. I checked. I also scald myself with live steam once in a while so I know it's easy to burn yourself. I could cite some industry association's standard for brewing temperature, but I know nobody cares.

The point I'm trying to make is not that coffee should be hot, because I know arguing that is futile.

The point is, that framing this as a disagreement about readily available facts is incorrect and if you habitually interpret people's opinions/values this way, it warps your sense of reality to your own detriment.

I think anyone who wants to prevent me from getting hot coffee is not a nice or reasonable person, and I feel threatened by any implication that I would be in the wrong for making hot coffee if someone else spilled it. But these are facts about me, not about the rest of the world. As such, you don't have to accept them, but you can't invalidate them with facts about the world either.

The whole “tort reform” business came about because injury lawyers were supporting democrats instead of republicans. There was an episode of Citations Needed about it.

https://medium.com/@CitationsPodcst/episode-107-pop-torts-an...

I supported them for a long time as well, but they've waded very far from their original waters. These days the ACLU is incredibly politically biased, and the issues they focus on are often non-issues or low-priority ones. I am disappointed to see them, for example, file lawsuits against schools that are trying to ensure that only biological women participate in sports divisions for women, so that those sports are competitive and fair.

The ACLU's social media accounts are a mess as well. Their postings come off as unhinged and sue-happy, and the fan base of commenters has become so one-sided, that I think the ACLU simply caters to that vocal audience now. Maybe the change is not solely attributable to that - there might also be a new generational wave of inside actors that simply operate the ACLU in a more ideological manner.

I agree that some of their work is still great. But unfortunately it's been enough of a change that upon weighing the good and bad, I had to finally pull the plug on my recurring donations too.

>These days the ACLU is incredibly politically biased

I was going to post the same in anticipation of the partisan downvotes that you're receiving. Without going into specifics, this sums it up nicely:

>It’s not that the left shouldn’t have opportunities to speak up against the president’s agenda -- of course it should. But the ACLU shouldn’t be its political bullhorn. The organization’s legal independence gave it special standing. By falling in line with dozens of other left-leaning advocacy groups, the ACLU risks diminishing its focus on civil liberties litigation and abandoning its reputation for being above partisanship

One issue in particular is the ACLU's interpretation of the second amendment, which they do not fight for with the same fervor as the first.

1.https://www.realclearpolitics.com/articles/2018/02/08/the_ac...

Their lack of support for the second amendment is the only reason I don’t support them. It proves that they’re politically slanted and it truly for my civil rights. Unfortunately the NRA has drifted from their core issue to bashing non-conservative candidates. I also can’t support them.

>I am disappointed to see them, for example, file lawsuits against schools that are trying to ensure that only biological women participate in sports divisions for women

Trans women are women. These schools are denying this with this idea, so it seems like a good use of the ACLU's resources to me. Morality aside, I don't know about the legal aspects of defending that principle in the US, but presumably the ACLU have some ground.

>, so that those sports are competitive and fair.

[drifting off the OP topic, but...] shouldn't school sports be about inclusion? what is your ideal of fairness here? For me, the pursuit and rewarding of certain idealised body types selected by narrow athletic criteria reminiscent of pageantry has never been 'fair' for anybody..

School sports ought to focus on inclusive physical self-improvement, help kids develop cooperative skills, resolve conflicts and work together for a common goal, that sort of thing.

> Trans women are women

Trans women are women from a gender perspective, and not from a biological perspective. Unfortunately, the biological perspective is what the separations of men's and women's sports a thing, because of inherent biological sexual differences (primarily testosterone). I mean, if they want to get around the issue, then let's go all in - disband men's/women's sports and just have sports. Then anyone can play with anyone. That's never going to happen though, for political, cultural, and safety reasons, so we're stuck with a situation where gender is bumping up against biological sex hard.

> Trans women are women from a gender perspective, and not from a biological perspective.

From a biological perspective, trans-people lie in-between male and female. Transwomen are at an increased risk of breast cancer than cis-men while a lower risk prostate cancer. Transwomen (after years of estradiol) have significantly less muscle mass than cis-men, while, transmen have much more muscle mass than cis-women. All of the differences biologically are the result of sex hormones and the time at which they're introduced (pre-natal or puberty) and the time of exposure.

That said, I don't think it's wise for trans-women to play in serious sports. If they win, they won't get the credit, they'll be told it's because they're trans. If they lose, well, then no one cares and it doesn't make the headlines. It's unfair and unjust, but honestly, sports have never been fair or just. Especially the Olympics, they're a selection ritual for celebrating people who are genetically optimized for some specific task. I don't understand why it exists except for out of tradition.

Yes, trans women are women. Agreed.

The challenge is that trans women are women who used to have comparatively vast quantities of testosterone in their bodies, giving them dramatically higher bone density along with all of the other side effects of testosterone on the body.

When they transition fully, the estrogen has a side-effect of preserving the bone density they had previously. Specifically, this gives them a massive advantage in combat sports like MMA. I won't get into the other musculoskeletal aspects that happen.

It's a vexing problem, because I don't like the idea of trans women not being treated like women. It sucks to think about that. It also sucks for non-trans women who are getting their skulls fractured in fights.

The reality is that fans of non trans female athletes aren't going to accept this. When a tiny percentage of the population is trans, and suddenly trans women start winning at the highest levels of female sports at an insanely disproportionate amount, it clearly indicates that being trans provides a massive advantage in female sports. I haven't found any cases of trans men dominating in male sports.

I'm pro trans rights, and also know that my view on this is deeply unpopular in the trans community. I'm not sure what the answer is for this. Sometimes, reality doesn't mesh with our ideals.

I think the answer has to be to find a different way to categorize people into groups for sports competitions.

Would it work to just group people by bone density and weight class?

Or for MMA, just some sort of scale of ass-kickingness. I would have to be in the can't punch out of a wet paper bag class, which I don't think would get shown on TV. ;)

Or you know, have all individuals compete with each other regardless of gender. And if we're not happy with the above suggestion, then we need to define why such a competition would be unfair. Once we can answer that "why" without gender or sex being involved, then everyone will be happy because it'll be as fair as can be had in a diverse society with a range of opinions. That'll include people that are not convinced on the gender/sex debate.

The problem now is that we're being polarized on an emotional issue with both sides being demonized. E.g. One side says the other is hateful of trans people, the other side says trans is being pushed on them unfairly, etc. If we rather focus on the facts and amoral concepts (e.g. testosterone count), and drive opinion/policy based on that, there is good chance that we can all coexist without having to force opinions and understanding on both sides where it's largely unwanted. Only then can we come together and have a single, happy view on the topic as a society.

Everyone discussing this issue needs to be very upfront and clear on whether they're talking about sex or gender. Sports leagues are explicitly about biological sex, and not gender. Your statement is not applicable here and is often used to add confusion and outrage while preventing any forward progress.

Perhaps there needs to be new vocabulary to describe sex and gender separately but that's a different topic. As for fairness, this is the best we can do on a broad scale between male and female because of the vast differences. After that we leave it to the individual to decide which sport best fits their physicality and interests.

Isn't their purpose to be politically biased?

Just because you share the bias doesn't make it unbiased

This.

Huge fan of the ACLU in general, but some of the highly unpopular but incredibly important work they used to do, for example defending neo-Nazi groups right to protest, has been discarded in favor of popular causes.

While I understand why they have gone this way (who wants to defend Nazis in court?) it was a very important symbol of the organizations insane dedication to civil liberties. Taking a principled stand of preserving freedoms for those who are deeply, deeply unpopular is inconvenient and essential.

Keep in mind the ACLU has a lot of local affiliates. All to often national orgs absorb the fundraising, while local chapters languish.

So does it imply that Amazon are not ready yet, so instead of saying "wait wait for one more year (please?)" they try to make a publicity stunt pretending to be good citizens?

> ... the software incorrectly matched 28 members of Congress, identifying them as other people who have been arrested for a crime.

Lol, that's because it's Amazon AI. Do you expect better from the makers of Alexa?

It's not like Amazon is bad at AI. The problem they're solving for is legitimately hard, which is why it's so frustrating that it's been put in production in a way that can really harm people.

If I'm not mistaken there's a configurable detection threshold and/or a match score.

But most likely people and organizations will think this work like the movies.

I wonder what would be the score of the actual faces if we added them to the test set (faces, but not the same photos). Would bigger test sets have photos that match the targets more than themselves?

> Nearly 40 percent of Rekognition’s false matches in our test were of people of color, even though they make up only 20 percent of Congress.

What percentage of the arrest photos were people of color? Was it significantly more or less than the 20 percent people of color in congress or about the same?

Do you understand what a badly designed experiment this is?

Depending on the outcome you desire, this could be a perfectly designed activity.

Sounds like Amazon will use the year to improve their poor performing software to me.

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