Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work

https://fed.brid.gy/r/https://www.techdirt.com/2026/02/03/turns-out-they-didnt-really-want-you-to-bring-your-whole-self-to-work/

Pluralistic: The enshittification of labor (07 Nov 2025)

https://fed.brid.gy/r/https://pluralistic.net/2025/11/07/postwar-social-contract/

Mind the Gap: Gender-based Differences in Occupational Embeddings
https://aclanthology.org/2025.gebnlp-1.7.pdf
"Across five state-of-the-art multilingual models and seven reference-set configurations, up to 82% of gendered pairs received divergent Top-5 suggestions. These differences involved distinct occupational codes that sometimes crossed major #KldB group

.…gendered job titles—such as Autor vs. Autorin —often lead to different occupation codes, despite having identical meanings. Our findings underscore the importance of grounding #NLP innovations in language-specific sociolinguistic knowledge. Without rigorous attention to linguistic structure and social context, these tools risk perpetuating systemic biases—particularly in settings where semantic equivalence is masked by morphological variation. Addressing such challenges is crucial not only for the technical refinement of NLP systems, but for ensuring that their real-world applications advance rather than hinder equity"
#jobtech #gender #discrimination #LaborEconomics #llm

Measuring Gender Bias in Job Title Matching for Grammatical Gender Languages
https://www.arxiv.org/pdf/2509.13803
"… propose a methodology to measure gender bias in a high-impact #NLP application in the human resources domain: job title matching. Using an existing test set in English for this task, we have generated gender-annotated analogous corpora in four languages with grammatical gender, and addressed the evaluation of #genderBias as ranking comparison controlling for gender. Additionally, we establish baselines and confirm that this type of bias already exists in out-of-the-box pre-trained models, which are often used as the core for developing job title matching applications.

Finding a trade-off between model performance and #gender #bias is an important issue to address when developing and selecting job matching models for deployment. On the one hand, choosing a model with apparent good performance but that in turn shows a considerable gender gap may not only be ethically questionable, but it may also result in reputation and even legal consequences on the company responsible for it."
#llm #jobtech #discrimination #LaborEconomics