AI is made from vast amounts of natural resources, fuel, and human labor. And it’s not intelligent in any kind of human intelligence way. It’s not able to discern things without extensive human training, and it has a completely different statistical logic for how meaning is made. Since the very beginning of AI back in 1956, we’ve made this terrible error, a sort of original sin of the field, to believe that minds are like computers and vice versa. We assume these things are an analog to human intelligence, and nothing could be further from the truth.
This builds upon her work a few years ago exploring the life cycle of an Amazon Echo.
In an extract published in The Atlantic, Crawford addresses concerns associated with emotion recognition:
This is the danger of automating emotion recognition. These tools can take us back to the phrenological past, when spurious claims were used to support existing systems of power. The decades of scientific controversy around inferring emotional states consistently from a person’s face underscores a central point: One-size-fits-all “detection” is not the right approach. Emotions are complicated, and they develop and change in relation to our cultures and histories—all the manifold contexts that live outside the AI frame.
For a different introduction, Zan Rowe leads dives into Crawford’s atlas of sound.