I think there might be. When ML fails the only individual capable of noticing is someone who understands the math. When code breaks often the "lay" user notices. The result is obvious to a novice. When ML fails it looks like a duck, quakes like a duck but after multiple years of study its immediately recognizable as an antelope. Though to disagree with my own point, security vulnerabilities have a similar profile. In essence, to all but the highly trained the difference is imperceptible.
>"When code breaks often the "lay" user notices. The result is obvious to a novice."
That depends "how" it breaks. As a novice coder myself, I've had things go wrong that I don't notice or can't identify, and it looks like my program is running fine.
I think that's the parent's point: it might be stupid to implement crappy macho learning models into production, but it isn't worrisome. It's expected.
I hear ya. I knew that assertion was going to draw some criticism as its a judgement call about where we draw the line. Who's a novice and what's obvious? However I can't get away from my nagging impression that statistical validity is not inherently clear to the absolute best practitioners. Causality is the goal, and its notoriously difficult, even for world class minds. In my experience the only similar effervescent specter for software development is in security. Such circumstance,seem to me, to require great humility and introspection about ones abilities, but I suppose a little of that would go a long way in general too!
