Algorithmic Monocultures in Hiring
Fuente:
arXiv
Saved in:
| Main Authors: | Bommasani, Rishi, Bana, Sarah H., Creel, Kathleen A., Jurafsky, Dan, Liang, Percy |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
by: Toups, Connor, et al.
Published: (2023)
by: Toups, Connor, et al.
Published: (2023)
Ecosystem Graphs: The Social Footprint of Foundation Models
by: Bommasani, Rishi, et al.
Published: (2023)
by: Bommasani, Rishi, et al.
Published: (2023)
The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy
by: Bommasani, Rishi
Published: (2025)
by: Bommasani, Rishi
Published: (2025)
The 2024 Foundation Model Transparency Index
by: Bommasani, Rishi, et al.
Published: (2024)
by: Bommasani, Rishi, et al.
Published: (2024)
Do AI Companies Make Good on Voluntary Commitments to the White House?
by: Wang, Jennifer, et al.
Published: (2025)
by: Wang, Jennifer, et al.
Published: (2025)
Foundation Model Transparency Reports
by: Bommasani, Rishi, et al.
Published: (2024)
by: Bommasani, Rishi, et al.
Published: (2024)
The 2025 Foundation Model Transparency Index
by: Wan, Alexander, et al.
Published: (2025)
by: Wan, Alexander, et al.
Published: (2025)
Language model developers should report train-test overlap
by: Zhang, Andy K, et al.
Published: (2024)
by: Zhang, Andy K, et al.
Published: (2024)
Beyond Release: Access Considerations for Generative AI Systems
by: Solaiman, Irene, et al.
Published: (2025)
by: Solaiman, Irene, et al.
Published: (2025)
Strategic Hiring under Algorithmic Monoculture
by: Baek, Jackie, et al.
Published: (2025)
by: Baek, Jackie, et al.
Published: (2025)
Effective Mitigations for Systemic Risks from General-Purpose AI
by: Uuk, Risto, et al.
Published: (2024)
by: Uuk, Risto, et al.
Published: (2024)
Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
by: Fabris, Alessandro, et al.
Published: (2023)
by: Fabris, Alessandro, et al.
Published: (2023)
HumT DumT: Measuring and controlling human-like language in LLMs
by: Cheng, Myra, et al.
Published: (2025)
by: Cheng, Myra, et al.
Published: (2025)
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
by: Cheng, Myra, et al.
Published: (2026)
by: Cheng, Myra, et al.
Published: (2026)
AnthroScore: A Computational Linguistic Measure of Anthropomorphism
by: Cheng, Myra, et al.
Published: (2024)
by: Cheng, Myra, et al.
Published: (2024)
STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports
by: McCaslin, Tegan, et al.
Published: (2025)
by: McCaslin, Tegan, et al.
Published: (2025)
The Limits of AI Data Transparency Policy: Three Disclosure Fallacies
by: Shen, Judy Hanwen, et al.
Published: (2026)
by: Shen, Judy Hanwen, et al.
Published: (2026)
Dialect prejudice predicts AI decisions about people's character, employability, and criminality
by: Hofmann, Valentin, et al.
Published: (2024)
by: Hofmann, Valentin, et al.
Published: (2024)
NeurIPS should lead scientific consensus on AI policy
by: Bommasani, Rishi
Published: (2025)
by: Bommasani, Rishi
Published: (2025)
Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
by: Cheng, Myra, et al.
Published: (2025)
by: Cheng, Myra, et al.
Published: (2025)
The Silent Curriculum: How Does LLM Monoculture Shape Educational Content and Its Accessibility?
by: Priyanshu, Aman, et al.
Published: (2024)
by: Priyanshu, Aman, et al.
Published: (2024)
ELEPHANT: Measuring and understanding social sycophancy in LLMs
by: Cheng, Myra, et al.
Published: (2025)
by: Cheng, Myra, et al.
Published: (2025)
Trustworthy Social Bias Measurement
by: Bommasani, Rishi, et al.
Published: (2022)
by: Bommasani, Rishi, et al.
Published: (2022)
What can large language models do for sustainable food?
by: Thomas, Anna T., et al.
Published: (2025)
by: Thomas, Anna T., et al.
Published: (2025)
From Protoscience to Epistemic Monoculture: How Benchmarking Set the Stage for the Deep Learning Revolution
by: Koch, Bernard J., et al.
Published: (2024)
by: Koch, Bernard J., et al.
Published: (2024)
Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions
by: Bhattacharya, Aditya, et al.
Published: (2025)
by: Bhattacharya, Aditya, et al.
Published: (2025)
AI-exposed jobs deteriorated before ChatGPT
by: Frank, Morgan R., et al.
Published: (2026)
by: Frank, Morgan R., et al.
Published: (2026)
Belief in the Machine: Investigating Epistemological Blind Spots of Language Models
by: Suzgun, Mirac, et al.
Published: (2024)
by: Suzgun, Mirac, et al.
Published: (2024)
How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications
by: Sharma, Gauri, et al.
Published: (2026)
by: Sharma, Gauri, et al.
Published: (2026)
Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
by: Gerszberg, Nina, et al.
Published: (2026)
by: Gerszberg, Nina, et al.
Published: (2026)
Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models
by: Rao, Pooja S. B., et al.
Published: (2025)
by: Rao, Pooja S. B., et al.
Published: (2025)
Irrelevant Alternatives Bias Large Language Model Hiring Decisions
by: Valkanova, Kremena, et al.
Published: (2024)
by: Valkanova, Kremena, et al.
Published: (2024)
Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects
by: Gallegos, Isabel O., et al.
Published: (2025)
by: Gallegos, Isabel O., et al.
Published: (2025)
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
by: Wang, Ze, et al.
Published: (2024)
by: Wang, Ze, et al.
Published: (2024)
Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring
by: Nghiem, Huy, et al.
Published: (2026)
by: Nghiem, Huy, et al.
Published: (2026)
Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/Résumé Evaluations
by: Rozado, David
Published: (2025)
by: Rozado, David
Published: (2025)
Verbalizing LLMs' assumptions to explain and control sycophancy
by: Cheng, Myra, et al.
Published: (2026)
by: Cheng, Myra, et al.
Published: (2026)
Algorithmic Monoculture and its Critics
by: Hedden, Brian, et al.
Published: (2026)
by: Hedden, Brian, et al.
Published: (2026)
On the Societal Impact of Open Foundation Models
by: Kapoor, Sayash, et al.
Published: (2024)
by: Kapoor, Sayash, et al.
Published: (2024)
AI & Data Competencies: Scaffolding holistic AI literacy in Higher Education
by: Kennedy, Kathleen, et al.
Published: (2025)
by: Kennedy, Kathleen, et al.
Published: (2025)
Similar Items
-
Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
by: Toups, Connor, et al.
Published: (2023) -
Ecosystem Graphs: The Social Footprint of Foundation Models
by: Bommasani, Rishi, et al.
Published: (2023) -
The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy
by: Bommasani, Rishi
Published: (2025) -
The 2024 Foundation Model Transparency Index
by: Bommasani, Rishi, et al.
Published: (2024) -
Do AI Companies Make Good on Voluntary Commitments to the White House?
by: Wang, Jennifer, et al.
Published: (2025)