> The Like Button is a classic example of bounding because it reduces your thoughts and feelings about a piece of content to a thumbs up
This is an interesting thought. It does simplify expression, but in sum I don't think it's that much different than life before the like button.
We press the like button all the time in real life. Giving thumbs up, a quick nod of the head, or clapping.
From that standpoint, the like button is just the digital manifestation of how we showed engagement with "content".
Where I see we run into problems is that a "like" has different meanings depending on the person and context. Unfortunately algorithms aren't able to fully understand these nuances yet, so we end up with the same stream of youtube videos in our feed or the same 5 songs in our pandora playlist. Which means on the backend we're stuck with a certain set of "almost-but-not-quite-right" labels in a system which will then make "almost-but-not-quite-right" decisions.
Folks at the edges may get incorrectly lumped into the wrong group and, in milder cases, receive content they aren't interested in, in extreme cases, be flagged as a "potential terrorist".
It's tough. I do think my life is generally better off with intelligent algos removing friction, aiding in discovery, and anticipating my needs. But to do so, those same algos need an almost infinite amount of info about me to be able to infer context and nuance.
> > The Like Button is a classic example of bounding because it reduces your thoughts and feelings about a piece of content to a thumbs up
> We press the like button all the time in real life. Giving thumbs up, a quick nod of the head, or clapping.
Oh no, I don't think real life expressions can be summed or approximated into a binary thumbs up vs no thumbs up (there's no thumbs down). For example, we express a lot more by how eagerly we raise our hands while giving a thumbs up, or how our eyes approve (or disapprove) while giving the quick nod, or how hard (or sarcastically mild) we clap.
I think there is always an amount of uncertainty in real life expression that gets lost the moment we transform them into certain form.
Agree! A lot of nuance can be lost in when converting to digital.
My point is more about the way people use the like button has corresponding actions in "real life".
A thumbs up can be used to convey a variety of emotions. Happiness, irony, and other meanings depending on culture.
However on FB, the like button has an implied set of narrow usage. You use it to acknowledge, approve, like things. It's not as useful as in real-life because you can't as easily interpret context or add accompanying signals like a furrowed brow or a smile.
The usage set is more narrow/different and I think most people recognize that when they give a thumbs up on FB vs. when they do so in real life.
This is an interesting thought. It does simplify expression, but in sum I don't think it's that much different than life before the like button.
We press the like button all the time in real life. Giving thumbs up, a quick nod of the head, or clapping.
From that standpoint, the like button is just the digital manifestation of how we showed engagement with "content".
Where I see we run into problems is that a "like" has different meanings depending on the person and context. Unfortunately algorithms aren't able to fully understand these nuances yet, so we end up with the same stream of youtube videos in our feed or the same 5 songs in our pandora playlist. Which means on the backend we're stuck with a certain set of "almost-but-not-quite-right" labels in a system which will then make "almost-but-not-quite-right" decisions.
Folks at the edges may get incorrectly lumped into the wrong group and, in milder cases, receive content they aren't interested in, in extreme cases, be flagged as a "potential terrorist".
It's tough. I do think my life is generally better off with intelligent algos removing friction, aiding in discovery, and anticipating my needs. But to do so, those same algos need an almost infinite amount of info about me to be able to infer context and nuance.