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.
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
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.
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?
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 (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.
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
: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”
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?
I have to agree with you one hundred, /u/percent
:)
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 (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. ↩︎