Technology

37720 readers

548 users here now

A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.

Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.

Subcommunities on Beehaw:

This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.

founded 2 years ago

MODERATORS

alyaza@beehaw.org

TheRtRevKaiser@beehaw.org

gyrfalcon@beehaw.org

rs5th@beehaw.org

Los@beehaw.org

coldredlight@beehaw.org

SemioticStandard@beehaw.org

TheRtRevKaiser@kbin.social

remington@beehaw.org

Gender, Race, and Intersectional Bias in Resume Screening via Language Model (www.geekwire.com)

submitted 2 weeks ago by Gaywallet@beehaw.org to c/technology@beehaw.org

4 comments fedilink hide all child comments

Direct link to paper: https://ojs.aaai.org/index.php/AIES/article/view/31748/33915

you are viewing a single comment's thread
view the rest of the comments

[–] GenderNeutralBro@lemmy.sdf.org 7 points 2 weeks ago

Interesting read, thanks! I'll finish it later, but already this bit is quite interesting:

Without access to gender, the ML algorithm over-predicts women to default compared to their true default rate, while the rate for men is accurate. Adding gender to the ML algorithm corrects for this and the gap in prediction accuracy for men and women who default diminishes.