You sometimes hear that once AI bias is sufficiently “fixed,” the technology can be much more ubiquitous. You write that this argument is problematic. Why?
One of the big issues I have with this argument is this idea that somehow AI is going to reach its full potential, and that that’s the goal that everybody should strive for. AI is just math. I don’t think that everything in the world should be governed by math. Computers are really good at solving mathematical issues. But they are not very good at solving social issues, yet they are being applied to social problems. This kind of imagined endgame of Oh, we’re just going to use AI for everything is not a future that I cosign on.
In your book, you critique the phrase “black box” in reference to machine learning, arguing that it incorrectly implies it’s impossible to describe the workings inside a model. How should we talk about machine learning instead?
That’s a really good question. All of my talk about auditing sort of explodes our notion of the “black box.” As I started trying to explain computational systems, I realized that the “black box” is an abstraction that we use because it’s convenient and because we don’t often want to get into long, complicated conversations about math. Which is fair! I go to enough cocktail parties that I understand you do not want to get into a long conversation about math. But if we’re going to make social decisions using algorithms, we need to not just pretend that they are inexplicable.