[Kenji]

Some of the best image-recognition systems, for example, can successfully distinguish dog breeds, yet remain capable of major blunders, like mistaking a simple pattern of yellow and black stripes for a school bus.

That's exactly the problem. Robots lack sanity checks because they lack real understanding. If you cannot recognize an object that is far away, you are instantly aware of your inability to identify this object. A computer just runs its code over it and outputs complete garbage, and this nonsense then enters the system and does who knows what damage.

Plausibility checks are incredibly complex! If you are in central Europe and you are not in a zoo and you see a leopard fur pattern, it's probably not the living animal! And so on.

[GuB-42]

Image recognition system just recognize images. They essentially do the first pass of what your brain can do.

You too can mistake yellow and black stripes for a school bus or see an actual leopard in Poland. That's when you put what you've seen in context that you rule out the idea. And if you really want to see something in a picture, you will, especially with faces.

It is no different with computers. You train your algorithm so see school buses exclusively and it will see school buses everywhere. Conversely can also teach it context, for example by taking account of the webpage hosting the image.

Computers algorithms usually have a confidence rating too. They can tell "definitely a school bus (99%)" or "looks vaguely like a school bus (30%), but it may also be a wasp (10%)", so they can be aware of their own flaws. In fact, confidence intervals are often a key part of machine learning.