Use cases gone wild with Dunning-Kruger AI

One consequence of the current genAI hype is companies, large and small, rushing to apply it to use cases that are either inappropriate or for which the technology is not yet ready. Of course, the more influential the company, the more harm it can cause. Reddit user Fucksmith gamed the Internet by way of Google Overview AI with his/her comment from 11 years ago. The problem is that Google Overview AI suffers from a gigantic Dunning-Kruger effect: incompetence + infinite self-confidence (or authority). People will follow its advice. A child will mix (non-toxic?) glue into the sauce, just like someone might inject themselves with bleach to avert disease.

So it is worth asking at this point, what is not a good use case? I think the answer is simple but it will be hard to put the genie back in the bottle:

👉 If anyone (or anything) is going to follow blindly the advice of an algorithm and decisions based on its faulty outputs can lead to harm, then it is not a good use case, for now.

Sadly, that simple rule of thumb would eliminate not just most current use cases but also a lot of questionable applications of machine learning more generally, predictive parole or credit scoring being examples that come to (my) mind.

In the section above, the word blindly is critical, because, in my view, hashtag#criticalthinking + hashtag#genAI can be the winning equation. The best use cases are those where genAI can help expand our minds.