These Women Tried to Warn Us About AI | Hacker News Reader

These Women Tried to Warn Us About AI

rollingstone.com

7 pointscratermoon6 comments

This article feels incredibly baited and regressive?

"not enough black people in AI" - can we get past this already? I'm a person of color and this is beyond pandering and borderline offensive to those of us who don't seek to publicly engage in this kind of "progress".

I'm white, and I'm curious -- do you think this isn't a problem?

Not at all, science is science math is math. Black women playing the victim card time and time again makes us all look like children who can't reason with the real world. It's beyond embarrassing.

Wanting attention and pursuing excellence are two very different ways to go about your professional or academic career. Looking for perfect racial distributions in all fields is beyond a fool's errand.

> Black women playing the victim card

I guess I didn't see it as playing the victim card as much as it was a call out of how the lack of diversity in the specific field of AI can have profound effects on how we understand and assess performance of these models

> Looking for perfect racial distributions in all fields is beyond a fool's errand.

I agree with that in principle but I think it is equally foolish to never ask how the racial distribution of a specific field might affect major advances and technologies coming out of that field

> can we get past this already?

I'd love to. Do you have any thoughts about the article past the second paragraph? As a person of color, do Seeta Peña Gangadharan's findings resonate with you?

For some reason another copy of this was flagged (https://news.ycombinator.com/item?id=37121000) and I wanted to respond to a thread there >> An entire hugely important potentially world changing field not having representation from a huge swath of the population certainly seems problematic. >I assume lots of groups are underrepresented, depending on how you slice up humanity. Why is that a problem in and of itself? Ensuring training data is representative is a fundamental technique of data science that you can implement regardless of skin color.

A representative sample means that any way you "slice up humanity", if you slice your training data the same way the proportions of slices would stay the same. Anyone of any skin color can implement that but they must do so taking racial proportions of the general population into account.

>> And in terms of the second part; the problems with such widespread usage are many fold, with detrimental bias and lack of representation as one of the many folds.

>But what does that have to do with who is working on the AI systems?

This is the meatier question but in my opinion, it matters because folks who are underrepresented can often (read: not always) have a better eye for underrepresentation in data. Another way to think of it is the unknown unknowns framing and privilege can result in unknown unknowns around what representative data really looks like.

Reply on news.ycombinator.com