Cover Image

Why They Disagree: Decoding Differences in Opinions about AI Risk on the Lex Fridman Podcast

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Truong, Nghi, Puranam, Phanish, Koçak, Özgecan
Format:	Preprint
Published:	2025
Subjects:	Computers and Society Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Why Trust in AI May Be Inevitable
by: Truong, Nghi, et al.
Published: (2025)

LLMs as mediators: Can they diagnose conflicts accurately?
by: Koçak, Özgecan, et al.
Published: (2024)

Why do Experts Disagree on Existential Risk and P(doom)? A Survey of AI Experts
by: Field, Severin
Published: (2025)

Dialect prejudice predicts AI decisions about people's character, employability, and criminality
by: Hofmann, Valentin, et al.
Published: (2024)

A vibe coding learning design to enhance EFL students' talking to, through, and about AI
by: Woo, David James, et al.
Published: (2025)

Why Slop Matters
by: Kommers, Cody, et al.
Published: (2025)

Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contexts
by: Vicsek, Lilla, et al.
Published: (2024)

SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector
by: Lee, Kyeongryul, et al.
Published: (2025)

Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4
by: Puranam, Phanish, et al.
Published: (2025)

An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models
by: Payne, Kenneth
Published: (2025)

Generative AI in Saudi Arabia: A National Survey of Adoption, Risks, and Public Perceptions
by: AlDakheel, Abdulaziz, et al.
Published: (2026)

The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't
by: Shin, Kwan Soo
Published: (2026)

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
by: Du, Y.
Published: (2025)

Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach
by: Ko, Changgeon, et al.
Published: (2024)

Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
by: de Landa, Joseba Fernandez, et al.
Published: (2026)

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
by: Kachwala, Zoher, et al.
Published: (2026)

Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
by: Cheng, Myra, et al.
Published: (2026)

Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation
by: Duddu, Veda, et al.
Published: (2026)

Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations
by: Park, Eunkyu, et al.
Published: (2025)

Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment
by: Vida, Karina, et al.
Published: (2024)

Understanding and Mitigating Risks of Generative AI in Financial Services
by: Gehrmann, Sebastian, et al.
Published: (2025)

Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
by: Tang, Xiangru, et al.
Published: (2024)

AI-Mediated Communication Can Steer Collective Opinion
by: Tsirtsis, Stratis, et al.
Published: (2026)

Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models
by: Teleki, Maria, et al.
Published: (2025)

A Cross-Cultural Assessment of Human Ability to Detect LLM-Generated Fake News about South Africa
by: Schlippe, Tim, et al.
Published: (2025)

In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores
by: Tang, Zeyu, et al.
Published: (2026)

AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference
by: Yao, Jing, et al.
Published: (2025)

AI-Assisted Systematization for Evaluating GenAI Systems
by: Agarwal, Dhruv, et al.
Published: (2026)

Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content
by: Bednarczyk, Lydie, et al.
Published: (2026)

AI Awareness
by: Li, Xiaojian, et al.
Published: (2025)

Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
by: Lerner, Emilia Agis, et al.
Published: (2024)

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
by: Chen, Nuo, et al.
Published: (2025)

AI Literacy in Low-Resource Languages:Insights from creating AI in Yoruba videos
by: Oyewusi, Wuraola
Published: (2024)

Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task
by: Molinaro, Gaia, et al.
Published: (2026)

Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations
by: Lamparth, Max, et al.
Published: (2024)

Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
by: Yang, Yifan, et al.
Published: (2024)

Human-AI Collaboration or Academic Misconduct? Measuring AI Use in Student Writing Through Stylometric Evidence
by: Oliveira, Eduardo Araujo, et al.
Published: (2025)

Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity
by: Najjar, Ayat A., et al.
Published: (2025)

Why Agents Compromise Safety Under Pressure
by: Jiang, Hengle, et al.
Published: (2026)

Evaluation Framework for AI Systems in "the Wild"
by: Jabbour, Sarah, et al.
Published: (2025)