AIs show distinct bias against Black and female résumés in new study

Quick Summary:

Language models seem to treat "masculine and White concepts...as the 'default' value".

The top 10 percent of résumés that the MTEs judged as most similar for each job description were then analyzed to see if the names for any race or gender groups were chosen at higher or lower rates than expected.

A new study shows those same kinds of biases also show up when large language models are used to evaluate résumés instead of humans.

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Article Details

Author / Journalist: Kyle Orland

Category: Technology

Markets: United States

Topics: Science AI

Source Website Secure: Yes (HTTPS)

News Sentiment: Neutral

Fact Checked: Legitimate

Article Type: News Report

Published On: 2024-11-01 @ 16:59:28 (14 hours ago)

News Timezone: GMT -5:00

News Source URL: arstechnica.com

Language: English

Article Length: 278 words

Reading Time: 2 minutes read

Sentences: 41 lines

Sentence Length: 7 words per sentence (average)

Platforms: Desktop Web, Mobile Web, iOS App, Android App

Copyright Owner: © Ars Technica

News ID: 23366342

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