Not really. What will have to happen is a readjustment for realizing that many models, especially made by people with little experience and training, will be wrong.
Right now it's the glory days of ML when nobody much has the ability to judge success. Unlike software engineering broadly, where these glory days just keep going, ML is all about measuring success. People will detect failures.
The real risk is when people systematically underestimate the risk like the copula thing occurring with the subprime market. That was anything bug untrained people using models—they would not have been as dangerous as they were if they weren't so damn good to begin with. This is a robustness failure, not a poorly trained workforce failure.
Right now it's the glory days of ML when nobody much has the ability to judge success. Unlike software engineering broadly, where these glory days just keep going, ML is all about measuring success. People will detect failures.
The real risk is when people systematically underestimate the risk like the copula thing occurring with the subprime market. That was anything bug untrained people using models—they would not have been as dangerous as they were if they weren't so damn good to begin with. This is a robustness failure, not a poorly trained workforce failure.