[chomp]

Maybe I'm not explaining it very well. Look, so things have meaning deeper than their face value. To use a really basic example, The number 14 means nothing, it's a number. The number 88 means nothing. In the same context, they mean something not good.

There are English words that as pieces, they don't mean anything except their face value. I can string words together that mean bad things that are harmful to real humans.

Gradients are not racist by themselves, they're just math. It's like saying multiplication is racist.

But I can use multiplication as a tool in a chain to create weighted averages to create a naive Bayesean classifier to reject people for home loans.

And so too can I misapply gradient descent as a part of a larger ML model that is racially biased. For instance, I could choose a loss function that when minimized, gives biased output despite less biased input. Or, I could accidentally settle on a local minimum on the gradient in my model. There's many naive implementations of an algorithm that will just be biased no matter the unbiased inputs.

So in summary, a gradient is just math and is not racist by itself. It's being used in an algorithmic tool chain that researchers are frequently using which potentially will always produce biased output no matter the inputs (but more often than not also with biased input).