Computational photography in general gets tricky because it relies on your answer to the question "Is a photograph supposed to reflect reality, or should it reflect human perception?"

We like to think those are the same, but they're not. Your brain only has a loose interest in reality and is much more focused on utility. Deleting the irrelevant, making important things literally bigger, enhancing contrast and color to make details stand out more.
You "see" a reconstruction of reality continuously updated by your eyes, which work fundamentally differently than a camera.

Applying different expose settings to different parts of an image, or reconstructing a video scene based on optic data captured over the entire video doesn't capture what the sensor captured but it can come much closer to representing what the human holding the camera perceived.
Low light photography is a great illustration of this, because we see a person walk from light to dark and our brains will shamelessly remember what color their shirt was and that grass is green and update your perception, as well as using a much longer "exposure" time to capture more light data to maintain color perception in low light conditions, even though we might not have enough actual light to make those determinations without clues.

I think most people want a snapshot of what they perceived at the moment.
I like the trend of the camera capturing the image, and also storing the "plain" image. There's also capturing the raw image data, which is basically a dump of the cameras optic sensor data. It's basically what the automatic post processing is tweaking, and what human photographers use to correct light balance and stuff.