Cover Image

Investigating Associational Biases in Inter-Model Communication of Large Generative Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dogan, Fethiye Irmak, Weiss, Yuval, Patel, Kajal, Cheong, Jiaee, Gunes, Hatice
Format:	Preprint
Published:	2026
Subjects:	Computers and Society Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment
by: Chiang, Sophie, et al.
Published: (2026)

Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement
by: Axelsson, Minja, et al.
Published: (2025)

Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching
by: Spitale, Micol, et al.
Published: (2025)

Uncertainty-based Fairness Measures
by: Kuzucu, Selim, et al.
Published: (2023)

Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours
by: Churamani, Nikhil, et al.
Published: (2024)

Designing Social Robots with Ethical, User-Adaptive Explainability in the Era of Foundation Models
by: Dogan, Fethiye Irmak, et al.
Published: (2026)

Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
by: Cameron, Joseph, et al.
Published: (2024)

AsyReC: A Multimodal Graph-based Framework for Spatio-Temporal Asymmetric Dyadic Relationship Classification
by: Tang, Wang, et al.
Published: (2025)

FAIRWELL: Fair Multimodal Self-Supervised Learning for Wellbeing Prediction
by: Cheong, Jiaee, et al.
Published: (2025)

Reimagining Social Robots as Recommender Systems: Foundations, Framework, and Applications
by: Huang, Jin, et al.
Published: (2026)

Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments
by: Checker, Saksham, et al.
Published: (2024)

Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction
by: Spitale, Micol, et al.
Published: (2024)

"Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas
by: Ding, Junchen, et al.
Published: (2025)

Sociodemographic Biases in Educational Counselling by Large Language Models
by: Adamczyk, Tomasz, et al.
Published: (2026)

Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistance
by: Ren, Ruiping, et al.
Published: (2024)

GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
by: Dogan, Fethiye Irmak, et al.
Published: (2024)

A Systematic Analysis of Biases in Large Language Models
by: Zhang, Xulang, et al.
Published: (2025)

Stakeholder Perspectives on Whether and How Social Robots Can Support Mediation and Advocacy for Higher Education Students with Disabilities
by: Markelius, Alva, et al.
Published: (2025)

Large Language Models are Geographically Biased
by: Manvi, Rohin, et al.
Published: (2024)

PRISM: A Methodology for Auditing Biases in Large Language Models
by: Azzopardi, Leif, et al.
Published: (2024)

Social Robotics for Disabled Students: An Empirical Investigation of Embodiment, Roles and Interaction
by: Markelius, Alva, et al.
Published: (2025)

Large Language Models Develop Novel Social Biases Through Adaptive Exploration
by: Wu, Addison J., et al.
Published: (2025)

Self-Blinding and Counterfactual Self-Simulation Mitigate Biases and Sycophancy in Large Language Models
by: Christian, Brian, et al.
Published: (2026)

Evaluating Large Language Models for Detecting Antisemitism
by: Patel, Jay, et al.
Published: (2025)

Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
by: Ożegalska-Łukasik, Natalia, et al.
Published: (2025)

Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
by: Jiang, Han, et al.
Published: (2024)

Beyond Vision: How Large Language Models Interpret Facial Expressions from Valence-Arousal Values
by: Mehra, Vaibhav, et al.
Published: (2025)

Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
by: Shieh, Evan, et al.
Published: (2024)

AccessEval: Benchmarking Disability Bias in Large Language Models
by: Panda, Srikant, et al.
Published: (2025)

LLM Biases
by: Han, Jinhui, et al.
Published: (2026)

FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)
by: Cheong, Jiaee, et al.
Published: (2025)

Decoding the Mind of Large Language Models: A Quantitative Evaluation of Ideology and Biases
by: Hirose, Manari, et al.
Published: (2025)

Prompt Perturbations Reveal Human-Like Biases in Large Language Model Survey Responses
by: Rupprecht, Jens, et al.
Published: (2025)

The Use of Artificial Intelligence Tools in Assessing Content Validity: A Comparative Study with Human Experts
by: Gurdil, Hatice, et al.
Published: (2025)

Large Language Models Show Human-like Social Desirability Biases in Survey Responses
by: Salecha, Aadesh, et al.
Published: (2024)

Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching
by: Cheong, Jiaee, et al.
Published: (2024)

Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
by: Feng, Duanyu, et al.
Published: (2023)

Born With a Silver Spoon? Investigating Socioeconomic Bias in Large Language Models
by: Singh, Smriti, et al.
Published: (2024)

Measuring and Mitigating Bias in Code Generated by Large Language Models
by: Chen, Yuxi, et al.
Published: (2026)

Inducing Human-like Biases in Moral Reasoning Language Models
by: Karpov, Artem, et al.
Published: (2024)