hypedarhypedar
feedtrendsdiscovershowcasearchive
login
login
login
FeedTrendsDiscoverShowcaseArchiveDashboard
Submit Showcase

Trending now

Agents + Productivity60Math + Games56Design + Ui + Agents51
View all trends →

hypedar

AI trend radar for developers. Catch emerging papers, repos, and discussions before the hype peaks.

AboutGitHubDiscord

By the makers of hypedar

Codepawl

Open-source tools for developers.

Explore our tools →
AboutPrivacyTermsX

© 2026 Codepawl

Built by Codepawl·© 2026

About·Terms·Privacy·Security

GitHub·Discord·X

feedtrendsdiscovershowcasearchive
← feed
arXiv12h ago
4.3

Investigating Counterfactual Unfairness in LLMs towards Identities through Humor

Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka, Seungbeen Lee, Jaeyoung Lee, Hyeju Jang, Alice Oh, Youngjae Yu

View original ↗

Analysis

Viral velocity
low
Implementation gapYES
Novelty5/10
Categorydiscussion
Topics
discussionfine-tuning

Opportunity Brief

Develop an automated evaluation suite that detects counterfactual unfairness in LLMs using humor as a probe. This can be a critical tool for safety-focused developers.

Suggested repo: fair-humor

"Test model bias using the ultimate litmus test: humor."

Estimated effort: 35h