Holy shit, I’m actually surprised how bad the rest of the comments are… And the volume of them!
I’ll try to TL;DR it for the lemmings, with formatting that is (hopefully) easy to understand for even the most rotten of brains.
TL;DR
The problem
Too many people are all calling 911 about the same emergency.
I’ll use this example scenario below: People keep driving past a burning car on a busy road, and many of them call 911. (This will continue to happen until an emergency responder arrives.)
Before implementing this tech
911 operators are all busy answering calls that are all reporting the same car fire
Long 911 hold time for someone with an emergency unrelated to the car fire
After implementing this tech
Bot: “Are you calling about the car fire on Seventh Street?”
If caller answers “yes”:
AI bot tells them that responders have already been dispatched
If caller answers anything other than “yes”:
Transfer to the next available human dispatcher
Greatly reduced hold time thanks to automated triage
If the critical failure point is accurately classifying “yes” or “not yes,” even the dumbest[1] models could handle that – and I doubt they use the dumbest models for 911 triage.
Even if it’s not 100% perfect every time, this still sounds like a net positive.
AI bot tells them that responders have already >been dispatched
So you’re gating whether or not a caller in an emergency gets to speak to a human based on the audio recognition of a robot?
Easy enough to make it so that if the bot doesn’t hear a yes or a no clearly it defaults to forwarding the call, but what happens when a person says no and the bot “hears” yes?
I believe I touched on that in my last sentence, but I can elaborate:
That will probably happen – neural networks are approximation algorithms. It’s a question of how often that happens.
What percentage of the calls get misclassified? And what’s the threshold percentage that would be needed for the triage bot to be a net positive?
It sounds like they have an idea of these numbers based on data collected from the non-emergency line, so it’s not like they’re just blindly jumping into this.
EDIT: I just realized that I did not actually answer your question of “what happens”…
I imagine the caller would just interrupt the AI’s answer (e.g. “No not that,” “HELP,” “Give me a human,” “FUCK!” etc.)? That seems like the natural thing to do.
To be clear: I don’t know anyone at Carbyne or OPCD. I can only offer speculation.
I don’t think there’s any net positive that would account for not answering an emergency call at all.
Exactly. That’s what they want to solve.
TL;DR: Even when callers reach the triage bot, they can still reach a human much faster than without the triage bot.
Comparing again:
WITHOUT the triage tech:
NOBODY (or nothing) answers the call for a long while, because the caller is stuck in a very long queue of calls waiting to tell them about the same emergency
WITH the triage tech:
AI bot answers the call instantly and probably knows how to help because the call is probably about the same emergency that 95% of the other calls are about
so 95% less spam for the human operators to get through
If the call is NOT about the same thing as the others, the caller can simply say that (i.e., “no”), and they reach a human within, say, 5-10 seconds because the operators aren’t busy trying to get through the spam calls
They chose this tech because it has already proven to be a net positive on their non-emergency line.
Do you honestly think that nobody has thought about that and solved that problem already? Even with all the engineers involved, and after all the real-world testing, you’re the first to have considered that scenario?
Even consumer-grade products like ChatGPT can be interrupted while talking. We’re talking about an AI implementation, not some rigid set of if...else statements.
You talk as if they don’t already have customer facing implementations in effect, and that those all suck and don’t work worth shit. You also seem to think the “engineers” involved are custom doing anything and not just some sales person slapping a half baked product on every problem. When you get paid per deployed program, everything looks like a problem to solve with said program.
This is an issue of not having enough 911 dispatchers, due to the unwillingness to pay for them. The idea of putting in a chatbot, that can not even speed up diction let alone have any empathy is just wildly inappropriate in this situation. The fact is that these LLMs will (like in current deployments) most of the time have to pass the call to a person, drastically increasing time on the phone before action is taken. This is already seen in almost all LLM supported call centers, but a 911 dispatch is not a telecom company and more time on the phone is more death, injury and suffering.
You talk as if they don’t already have customer facing implementations in effect,
Mind pointing to where I talk like that? I knew these have already been used in other customer-facing environments before ever commenting, so I’m happy to try to clarify, if needed.
and that those all suck and don’t work worth shit.
Got any sources from within the last year? I ask for the last year because “AI” (LLMs and the overall ecosystem) has become much more capable over the last ~year (maybe a little less, but close enough).
I’ve already seen some reports that are older and, unsurprisingly, terrible. Those earlier generations of LLMs definitely don’t seem like they’d be up to the task – and some cities even had the balls to adopt this tech back in 2023 😬
This is an issue of not having enough 911 dispatchers
Correct, but it’s not like they can just go to the 911 dispatcher store and pick up some dispatchers. The widespread shortages have been a problem since before transformer-based LLMs even existed.
This triage system is a mitigation, not a solution. It makes the bad problem less bad – not solved. Maybe someday there will be enough dispatchers. Unfortunately, we have not reached that “someday” yet.
due to the unwillingness to pay for them.
Source? Not saying you’re wrong – I’m only aware of the shortage because I was friends with a dispatcher. I just never really looked into why there’s a shortage, and now I’m curious.
The idea of putting in a chatbot, that can not even speed up diction
The goal is not to speed up diction – that would be more like “vertical scaling,” or “scaling up.” AI a bad choice for scaling that way, in most cases. AI is much better for “horizontal scaling,” or “scaling out” – so like 20 bots concurrently answering 1 call each, not 1 bot trying to speed-run through 20 calls serially.
The fact is that these LLMs will (like in current deployments)
By “current deployments,” do you mean current 911 deployments, or just things like customer service lines? There’s a huge difference in product requirements between those two. If done the same, then yes, that would be an absolute disaster. That’s not what this is though.
most of the time have to pass the call to a person, drastically increasing time on the phone before action is taken.
Where is this information from? I thought the problem was the surge of calls going to the call center to report the same thing (for example, people calling 911 when driving past a burning car). When that happens, the AI agents actually don’t have to pass most calls to a person, because most calls are about the same emergency (e.g. the car fire example). Did I misunderstand this?
911 dispatch is not a telecom company and more time on the phone is more death, injury and suffering.
Exactly. This system reduces hold times by filtering out the spam about the car fire, freeing up some operators in the understaffed team to deal with more emergencies.
The AI system is obviously slower than a well-staffed team of operators who can handle the call volume surges, but faster than an understaffed team that has to get through the spam. Unfortunately, they’re faced with the latter, so they found a way to at least mitigate the problem a bit.
Do you honestly think that nobody has thought about that and solved that problem already? Even with all the engineers involved, and after all the real-world testing,
That is the thought process of a child. If your defense of something with obvious issues is “Well I’m sure they know what they’re doing, because if they didn’t it would be bad”, I have some really, really bad news for you about the world.
If this was thought through by a capable adult, it would’t be implemented at all. The very fact that this solution is on the table is proof of the breakdown of rational thought. Or more likely, greased palms.
Your blind appeal to authority would hold more water if we haven’t already seen national adoption of AI products in emergency services that that have catastrophically failed to perform their basic functions, leading to real world harm. Audits of Flock cameras deployed in several cities found an error rate ranging from 33-70%. People are being arrested for things they didn’t do because the AI doesn’t work and nobody cares. People are being stalked because nobody thought of basic security controls.
We’re talking about an AI implementation, not some rigid set of if...elsestatements.
Even worse, we’re talking about an AI implementation of if…else statements. Regular if else statements are consistent. As soon as you throw AI into the mix they become unpredictable. You introduce a failure mode that didn’t exist before that can and will have real consequences.
So… you do assume that you’re the first to think of this problem, or…?
If you assume that your brain is somehow superior[1] to everyone involved in the R&D, engineering, data analysts/QC, etc., then this may come as a surprise to you: You’re not even the first Lemmy commenter to think of it.
Literally multiple people here already thought about that and commented before you did. They’re not the first either; the author of the article herself even thought about it.
This idea is among the most obvious potential problems that come to mind within seconds or minutes of just hearing about the concept. It’s not a dumb idea by any means (it’s completely valid), it’s just not a particularly smart or special one.
911 is not the first department in that city[2] to use this tech, and that city isn’t even the first to adopt it. It’s silly to assume that such an easy-to-solve problem hasn’t been addressed by now, or that it somehow has not yet happened in other deployments.
To be clear: I make no assumptions that your brain is not superior to theirs either – I couldn’t possibly know either way. AFAIK, I’ve never even met you nor them… But I think it would be a safe bet that you don’t know either. ↩︎
BTW, have you heard cajun accents? There’s no way this bridge hasn’t already been crossed during their 311 trials. ↩︎
Why does it have to be AI? Why can’t it be “press 1 for the highway car fire on 42, press 2 to be transferred to an operator”? Surely that’s much less likely to fail?
I believe the argument is that the probability of misclassifying an arbitrary segment of speech as the very particular answer “yes” is sufficiently low (with reasonably trained, current models) that we may ignore it under practical circumstances.
I find the non-zero probability of misclassifying in a little disquieting, but I suppose one way of looking at it is that this will probably increase the expected value of lives saved; the very probable case (calls are correctly filtered and a smaller proportion is passed through, allowing operators to respond to more new emergencies) may save a lot of lives, whereas the very improbable case (something that is not “yes” is misclassified as such) may endanger a few.
One thing that would worry me about just looking at expected values is the possibility of bias against a particular group of people or emergency type, but to me that seems unlikely in this case.
Edit: it also just occurred to me that if you are woefully unfortunate, you can probably just call again if you accept the assumption that there is a high probability the answer is yes given the model classified it as such. It might be more or less random chance?
Yeah I just don’t buy that there is a case where any “net gain” here is justified. These are people’s lives that you’re playing with by reducing them to what amounts to a balance sheet in the name of saving money.
The idea that you would create the possibility of denying someone emergency care that didn’t exist before, no matter how improbable (probably not even that improbable, given the propensity even the most advanced frontier AI models have for getting things wrong or hallucinating entirely despite simple instructions) just to save 10-15 seconds on average for other callers is not only absurd on its face, but morally bankrupt. You can personally ignore it because you don’t live there and it’s not the lives of you or your family at stake. I think those who this system could fail would not be able to ignore a flaw like that when it happens to them, and the idea that their peril was “highly improbable” will not be of much comfort to them.
The problem is that there are staffing shortages. The solution is hiring more staff. Trying to cheat our way out by implementing a system prone to unmitigatable flaws that could have life-altering or even life ending consequences isn’t a solution, it’s dystopian.
how long could the queue be? If someone can’t wait 60 seconds for the queue, but can take the 8 seconds to ask if it’s about X incident. I do imagine it will be smart enough to check their approximate location before giving the prompt
AI is absolutely not needed for this, and is only a liability.
They could say “press 1 if you are calling about X” or even “if you are calling about X, we are already aware of it. If not, you will be transferred to an operator in a moment”.
An automated system like this is easy to implement, easy to audit, and will fail in expected and managable ways.
AI is nondeterministic, and will fail randomly in very unexpected ways.
I think this could be updated by a single person for multiple developing situations up to 10 at a single time (which is unlikely for mass reports imo, I have no basis for this assumption however). Rather than needing an AI agent to update automatically
I (mostly) agree. I was just TL;DR’ing it for the shocking number of people who are commenting about things that are already addressed in the article that they didn’t bother reading.
I hesitate to agree with “absolutely” though. I don’t know enough about their situation to confidently have an opinion that strong[1].
AI can be useful for some dynamic situations – and 911 call centers can get pretty “dynamic” at times.
Imagine a busy 911 call center. Clippy appears (wearing a firefighter uniform) on an operator’s screen with a message like “The last 4 calls were about the same car fire. There are currently 9 calls in the queue. Want me to triage?”
It would take WAY less time for a busy operator to click “Yes” than it would to step away and go record some voice message, configure some automated thing, etc.
I only ever knew one dispatcher personally, and even in her town (much lower population than New Orleans’), they were overworked. ↩︎
Holy shit, I’m actually surprised how bad the rest of the comments are… And the volume of them!
I’ll try to TL;DR it for the lemmings, with formatting that is (hopefully) easy to understand for even the most rotten of brains.
TL;DR
The problem
I’ll use this example scenario below: People keep driving past a burning car on a busy road, and many of them call 911. (This will continue to happen until an emergency responder arrives.)
Before implementing this tech
After implementing this tech
If the critical failure point is accurately classifying “yes” or “not yes,” even the dumbest[1] models could handle that – and I doubt they use the dumbest models for 911 triage.
Even if it’s not 100% perfect every time, this still sounds like a net positive.
https://www.youtube.com/watch?v=ACmydtFDTGs ↩︎
So you’re gating whether or not a caller in an emergency gets to speak to a human based on the audio recognition of a robot?
Easy enough to make it so that if the bot doesn’t hear a yes or a no clearly it defaults to forwarding the call, but what happens when a person says no and the bot “hears” yes?
I believe I touched on that in my last sentence, but I can elaborate:
That will probably happen – neural networks are approximation algorithms. It’s a question of how often that happens.
What percentage of the calls get misclassified? And what’s the threshold percentage that would be needed for the triage bot to be a net positive?
It sounds like they have an idea of these numbers based on data collected from the non-emergency line, so it’s not like they’re just blindly jumping into this.
EDIT: I just realized that I did not actually answer your question of “what happens”…
I imagine the caller would just interrupt the AI’s answer (e.g. “No not that,” “HELP,” “Give me a human,” “FUCK!” etc.)? That seems like the natural thing to do.
To be clear: I don’t know anyone at Carbyne or OPCD. I can only offer speculation.
I don’t think there’s any net positive that would account for not answering an emergency call at all.
Can you really justify someone not being able to reach help at all in place of everyone being able to reach it albeit slower?
As a lifelong first responder, I couldn’t get behind something like this at all.
:sigh: Okay, I’ll try to break it down even more…
Exactly. That’s what they want to solve.
TL;DR: Even when callers reach the triage bot, they can still reach a human much faster than without the triage bot.
Comparing again:
WITHOUT the triage tech:
WITH the triage tech:
They chose this tech because it has already proven to be a net positive on their non-emergency line.
None of that applies if a caller just can’t get through at all because the bot mistook “no” for “yes”
Do you honestly think that nobody has thought about that and solved that problem already? Even with all the engineers involved, and after all the real-world testing, you’re the first to have considered that scenario?
Even consumer-grade products like ChatGPT can be interrupted while talking. We’re talking about an AI implementation, not some rigid set of
if...elsestatements.You talk as if they don’t already have customer facing implementations in effect, and that those all suck and don’t work worth shit. You also seem to think the “engineers” involved are custom doing anything and not just some sales person slapping a half baked product on every problem. When you get paid per deployed program, everything looks like a problem to solve with said program.
This is an issue of not having enough 911 dispatchers, due to the unwillingness to pay for them. The idea of putting in a chatbot, that can not even speed up diction let alone have any empathy is just wildly inappropriate in this situation. The fact is that these LLMs will (like in current deployments) most of the time have to pass the call to a person, drastically increasing time on the phone before action is taken. This is already seen in almost all LLM supported call centers, but a 911 dispatch is not a telecom company and more time on the phone is more death, injury and suffering.
Mind pointing to where I talk like that? I knew these have already been used in other customer-facing environments before ever commenting, so I’m happy to try to clarify, if needed.
Got any sources from within the last year? I ask for the last year because “AI” (LLMs and the overall ecosystem) has become much more capable over the last ~year (maybe a little less, but close enough).
I’ve already seen some reports that are older and, unsurprisingly, terrible. Those earlier generations of LLMs definitely don’t seem like they’d be up to the task – and some cities even had the balls to adopt this tech back in 2023 😬
Correct, but it’s not like they can just go to the 911 dispatcher store and pick up some dispatchers. The widespread shortages have been a problem since before transformer-based LLMs even existed.
This triage system is a mitigation, not a solution. It makes the bad problem less bad – not solved. Maybe someday there will be enough dispatchers. Unfortunately, we have not reached that “someday” yet.
Source? Not saying you’re wrong – I’m only aware of the shortage because I was friends with a dispatcher. I just never really looked into why there’s a shortage, and now I’m curious.
The goal is not to speed up diction – that would be more like “vertical scaling,” or “scaling up.” AI a bad choice for scaling that way, in most cases. AI is much better for “horizontal scaling,” or “scaling out” – so like 20 bots concurrently answering 1 call each, not 1 bot trying to speed-run through 20 calls serially.
By “current deployments,” do you mean current 911 deployments, or just things like customer service lines? There’s a huge difference in product requirements between those two. If done the same, then yes, that would be an absolute disaster. That’s not what this is though.
Where is this information from? I thought the problem was the surge of calls going to the call center to report the same thing (for example, people calling 911 when driving past a burning car). When that happens, the AI agents actually don’t have to pass most calls to a person, because most calls are about the same emergency (e.g. the car fire example). Did I misunderstand this?
Exactly. This system reduces hold times by filtering out the spam about the car fire, freeing up some operators in the understaffed team to deal with more emergencies.
The AI system is obviously slower than a well-staffed team of operators who can handle the call volume surges, but faster than an understaffed team that has to get through the spam. Unfortunately, they’re faced with the latter, so they found a way to at least mitigate the problem a bit.
That is the thought process of a child. If your defense of something with obvious issues is “Well I’m sure they know what they’re doing, because if they didn’t it would be bad”, I have some really, really bad news for you about the world.
If this was thought through by a capable adult, it would’t be implemented at all. The very fact that this solution is on the table is proof of the breakdown of rational thought. Or more likely, greased palms.
Your blind appeal to authority would hold more water if we haven’t already seen national adoption of AI products in emergency services that that have catastrophically failed to perform their basic functions, leading to real world harm. Audits of Flock cameras deployed in several cities found an error rate ranging from 33-70%. People are being arrested for things they didn’t do because the AI doesn’t work and nobody cares. People are being stalked because nobody thought of basic security controls.
Even worse, we’re talking about an AI implementation of if…else statements. Regular if else statements are consistent. As soon as you throw AI into the mix they become unpredictable. You introduce a failure mode that didn’t exist before that can and will have real consequences.
Yeah it’s definitely better to not answer all those calls
So… you do assume that you’re the first to think of this problem, or…?
If you assume that your brain is somehow superior[1] to everyone involved in the R&D, engineering, data analysts/QC, etc., then this may come as a surprise to you: You’re not even the first Lemmy commenter to think of it.
Literally multiple people here already thought about that and commented before you did. They’re not the first either; the author of the article herself even thought about it.
This idea is among the most obvious potential problems that come to mind within seconds or minutes of just hearing about the concept. It’s not a dumb idea by any means (it’s completely valid), it’s just not a particularly smart or special one.
911 is not the first department in that city[2] to use this tech, and that city isn’t even the first to adopt it. It’s silly to assume that such an easy-to-solve problem hasn’t been addressed by now, or that it somehow has not yet happened in other deployments.
To be clear: I make no assumptions that your brain is not superior to theirs either – I couldn’t possibly know either way. AFAIK, I’ve never even met you nor them… But I think it would be a safe bet that you don’t know either. ↩︎
BTW, have you heard cajun accents? There’s no way this bridge hasn’t already been crossed during their 311 trials. ↩︎
Why does it have to be AI? Why can’t it be “press 1 for the highway car fire on 42, press 2 to be transferred to an operator”? Surely that’s much less likely to fail?
https://infosec.pub/comment/23116617
I believe the argument is that the probability of misclassifying an arbitrary segment of speech as the very particular answer “yes” is sufficiently low (with reasonably trained, current models) that we may ignore it under practical circumstances.
I find the non-zero probability of misclassifying in a little disquieting, but I suppose one way of looking at it is that this will probably increase the expected value of lives saved; the very probable case (calls are correctly filtered and a smaller proportion is passed through, allowing operators to respond to more new emergencies) may save a lot of lives, whereas the very improbable case (something that is not “yes” is misclassified as such) may endanger a few.
One thing that would worry me about just looking at expected values is the possibility of bias against a particular group of people or emergency type, but to me that seems unlikely in this case.
Edit: it also just occurred to me that if you are woefully unfortunate, you can probably just call again if you accept the assumption that there is a high probability the answer is yes given the model classified it as such. It might be more or less random chance?
Yeah I just don’t buy that there is a case where any “net gain” here is justified. These are people’s lives that you’re playing with by reducing them to what amounts to a balance sheet in the name of saving money.
The idea that you would create the possibility of denying someone emergency care that didn’t exist before, no matter how improbable (probably not even that improbable, given the propensity even the most advanced frontier AI models have for getting things wrong or hallucinating entirely despite simple instructions) just to save 10-15 seconds on average for other callers is not only absurd on its face, but morally bankrupt. You can personally ignore it because you don’t live there and it’s not the lives of you or your family at stake. I think those who this system could fail would not be able to ignore a flaw like that when it happens to them, and the idea that their peril was “highly improbable” will not be of much comfort to them.
The problem is that there are staffing shortages. The solution is hiring more staff. Trying to cheat our way out by implementing a system prone to unmitigatable flaws that could have life-altering or even life ending consequences isn’t a solution, it’s dystopian.
how long could the queue be? If someone can’t wait 60 seconds for the queue, but can take the 8 seconds to ask if it’s about X incident. I do imagine it will be smart enough to check their approximate location before giving the prompt
AI is absolutely not needed for this, and is only a liability.
They could say “press 1 if you are calling about X” or even “if you are calling about X, we are already aware of it. If not, you will be transferred to an operator in a moment”.
An automated system like this is easy to implement, easy to audit, and will fail in expected and managable ways.
AI is nondeterministic, and will fail randomly in very unexpected ways.
I think this could be updated by a single person for multiple developing situations up to 10 at a single time (which is unlikely for mass reports imo, I have no basis for this assumption however). Rather than needing an AI agent to update automatically
I (mostly) agree. I was just TL;DR’ing it for the shocking number of people who are commenting about things that are already addressed in the article that they didn’t bother reading.
I hesitate to agree with “absolutely” though. I don’t know enough about their situation to confidently have an opinion that strong[1].
AI can be useful for some dynamic situations – and 911 call centers can get pretty “dynamic” at times.
Imagine a busy 911 call center. Clippy appears (wearing a firefighter uniform) on an operator’s screen with a message like “The last 4 calls were about the same car fire. There are currently 9 calls in the queue. Want me to triage?” It would take WAY less time for a busy operator to click “Yes” than it would to step away and go record some voice message, configure some automated thing, etc.
I only ever knew one dispatcher personally, and even in her town (much lower population than New Orleans’), they were overworked. ↩︎
I have to agree with you one hundred, /u/percent
:)