> 99% isn’t good enough for truly critical applications, especially when you don’t know for sure that it’s actually 99%; there’s no way to detect which 1% might be wrong; there’s no real path to 100%; and critically: there’s no one to hold responsible for getting it wrong.
AI also exposes the possibility of systemic error where humans would be stochastic.
A human might only identify the right number of rentable units from a spreadsheet (to pick an example from this article) 97% of the time when an AI might do it 99% of the time, but even the same human will have a different 3% error on each day. The consequences of failure are more limited and more dilute.
On the other hand, the AI may work perfectly right up until a holding company redesigns their data tables for the 100th time, whereupon it misreads every financial report with much more concentrated ill effect.
AI also exposes the possibility of systemic error where humans would be stochastic.
A human might only identify the right number of rentable units from a spreadsheet (to pick an example from this article) 97% of the time when an AI might do it 99% of the time, but even the same human will have a different 3% error on each day. The consequences of failure are more limited and more dilute.
On the other hand, the AI may work perfectly right up until a holding company redesigns their data tables for the 100th time, whereupon it misreads every financial report with much more concentrated ill effect.