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In those sorts of domains the best pitch for AI is as a failsafe. The model and the human probably make different errors, often a human will make mistakes due t

In those sorts of domains the best pitch for AI is as a failsafe. The model and the human probably make different errors, often a human will make mistakes due to simple inattention. This lets you substitute the model for some other process controls that you'd need to maintain 100% accuracy, e.g. instead of having the work reviewed by a second person, you can have model + person. In lots of business contexts, probably most, reducing variance is much more valuable than reducing mean expenses. Variance can halt downstream production, so the loss can be some huge amount of opportunity. And variance propagates through a supply chain, so your customers will hate variance in your output, as it may mean they have to ship the variance forward, which their customers hate. Plus if you allow your supply chain to get away with inconsistency, they can start to rob you with lower average quality and it will take you time to notice. If a company has been bothering to do some process manually and they haven't outsourced it to the cheapest humans possible, then they care more about low variance than low cost. Pitching these businesses a solution that lowers cost at the expense of unknown high variance is very unattractive. Instead, you want to tell them "I can reduce your variance even further! Here's what that would cost".

lqet 6y ago
> instead of having the work reviewed by a second person, you can have model + person But the argument still applies then - if only one incident occurs where a catastrophic mistake was not spotted by the model failsafe, and if later investigations show that the mistake could've been easily spotted by a second human, a human will be installed as a failsafe for the failsafe. I think the general problem is the following: if a human makes a grand mistake, it can usually be attributed to a temporal lack of care, or just random bad luck, or happened because the person was having a bad day, or... it is also generally understood that making a grand mistake will be such a shock for the person responsible that this person will most likely never make the same mistake again. On the other hand, if a machine makes a grand mistake, the intuition of the general public, trained by centuries of experience with techology, is that this machine will make the same mistake again and again and again, when prompted with the same input. If the model is not designed to learn from mistakes in production, this will of course actually be true.
- MaxBarraclough 6y ago
  > But the argument still applies then [...] a human will be installed as a failsafe for the failsafe. Surely this will depend on the cost of failure, and the cost of the human failsafe. Spellcheck in an email client helps prevent the minor embarrassment of typos and spelling errors in emails, and few emails are so consequential that it's worth having them carefully manually vetted.
  - lqet 6y ago
    Of course, this is why I wrote "catastrophic" above. I also would not call an email spellchecker a business use case. Regarding spellcheckers per se: I have worked with book publishing companies in the last few years, and I can assure you all major book publishers employ real humans for the final spellchecking before a book goes into print.
- fauigerzigerk 6y ago
  But the parent's point is that "the model and the human probably make different errors". That is frequently true. Take the job of a lifeguard for instance. A single mistake can be catastrophic, and yet we know that people have trouble staying completely focused for hours on end. AIs have no trouble staying focused and they can be trained to spot drowing swimmers pretty well even on a crowded beach. Having a human lifeguard plus an AI that alerts the lifeguard when it spots something suspicious could lead to better outcomes than employing two lifeguards.
  - kingkawn 6y ago
    This is very dependent on the false positive rate. Similar to the examples above about false negatives, if the AI gives false positives the lifeguards will stop paying attention to its feedback.
  - AlexandrB 6y ago
    > Having a human lifeguard plus an AI that alerts the lifeguard when it spots something suspicious could lead to better outcomes than employing two lifeguards. I disagree. I think you'll see the same thing as with drivers falling asleep while Tesla autopilot is running. The lifeguard will let the computer do all the monitoring since a low false negative rate combined with a low incidence rate means that most bodyguards will experience the computer being 100% reliable for weeks or months at a time. In fact it's not unreasonable that if the lifeguard sees someone drowning but the computer doesn't register it as such the lifeguard may question their own judgement based on experience.
woeirua 6y ago
Except that in most practical scenarios, most models make basic mistakes that no human would ever make. In most cases, people have a very low tolerance for errors that humans would not make.

lqet 6y ago

> instead of having the work reviewed by a second person, you can have model + person But the argument still applies then - if only one incident occurs where a catastrophic mistake was not spotted by the model failsafe, and if later investigations show that the mistake could've been easily spotted by a second human, a human will be installed as a failsafe for the failsafe. I think the general problem is the following: if a human makes a grand mistake, it can usually be attributed to a temporal lack of care, or just random bad luck, or happened because the person was having a bad day, or... it is also generally understood that making a grand mistake will be such a shock for the person responsible that this person will most likely never make the same mistake again. On the other hand, if a machine makes a grand mistake, the intuition of the general public, trained by centuries of experience with techology, is that this machine will make the same mistake again and again and again, when prompted with the same input. If the model is not designed to learn from mistakes in production, this will of course actually be true.

MaxBarraclough 6y ago
> But the argument still applies then [...] a human will be installed as a failsafe for the failsafe. Surely this will depend on the cost of failure, and the cost of the human failsafe. Spellcheck in an email client helps prevent the minor embarrassment of typos and spelling errors in emails, and few emails are so consequential that it's worth having them carefully manually vetted.
- lqet 6y ago
  Of course, this is why I wrote "catastrophic" above. I also would not call an email spellchecker a business use case. Regarding spellcheckers per se: I have worked with book publishing companies in the last few years, and I can assure you all major book publishers employ real humans for the final spellchecking before a book goes into print.
fauigerzigerk 6y ago
But the parent's point is that "the model and the human probably make different errors". That is frequently true. Take the job of a lifeguard for instance. A single mistake can be catastrophic, and yet we know that people have trouble staying completely focused for hours on end. AIs have no trouble staying focused and they can be trained to spot drowing swimmers pretty well even on a crowded beach. Having a human lifeguard plus an AI that alerts the lifeguard when it spots something suspicious could lead to better outcomes than employing two lifeguards.
- kingkawn 6y ago
  This is very dependent on the false positive rate. Similar to the examples above about false negatives, if the AI gives false positives the lifeguards will stop paying attention to its feedback.
- AlexandrB 6y ago
  > Having a human lifeguard plus an AI that alerts the lifeguard when it spots something suspicious could lead to better outcomes than employing two lifeguards. I disagree. I think you'll see the same thing as with drivers falling asleep while Tesla autopilot is running. The lifeguard will let the computer do all the monitoring since a low false negative rate combined with a low incidence rate means that most bodyguards will experience the computer being 100% reliable for weeks or months at a time. In fact it's not unreasonable that if the lifeguard sees someone drowning but the computer doesn't register it as such the lifeguard may question their own judgement based on experience.

woeirua 6y ago

Except that in most practical scenarios, most models make basic mistakes that no human would ever make. In most cases, people have a very low tolerance for errors that humans would not make.