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Cameron Jones

Stony Brook University

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Scholarly Papers (1)

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AI Epistemic Risks: Emerging Mechanisms & Evidence

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AI Epistemic Risks: Emerging Mechanisms & Evidence

Number of pages: 97 Posted: 04 Jun 2026
Mick Yang, Stephen Casper, Jonathan Stray, Jasmine Li, Cameron Jones, Anna Gausen, Natasha Jacques, Brian Christian, Bálint Gyevnár, Hannah Kirk, Zhonghao He, Dan Zhao, Siao Si Looi, Joshua Levy, Kobi Hackenburg, Elizabeth Seger, Matt Kowal, Michelle Malonza, Luke Hewitt, Hause Lin, Maarten Sap, Dylan Hadfield-Menell, Thomas Costello, Reihaneh Rabbany, Jean-François Godbout, David Rand, Atoosa Kasirzadeh, Gordon Pennycook, Yoshua Bengio and Kellin Pelrine
University of Pennsylvania, Massachusetts Institute of Technology (MIT), University of California, Berkeley - Center for Human-Compatible AI, Cornell University, Stony Brook University, Imperial College London, University of Washington, University of Oxford, Carnegie Mellon University, University of Oxford, University of Cambridge, Massachusetts Institute of Technology (MIT), FAR.AI, FAR.AI, University of Oxford, Tony Blair Institute for Global Change, FAR.AI, ILINA Program, Stanford University, Massachusetts Institute of Technology (MIT), Carnegie Mellon University, Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science, Carnegie Mellon University, McGill University, University of Montreal - Université de Montreal, Cornell University, Carnegie Mellon University, Cornell University, University of Montreal - Department of Informatics and Operations Research and FAR.AI
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AI, LLM, epistemic, risks, cognitive, neuroscience, reasoning, decision-making, learning, education, feedback loops, echo chambers, human-AI, AI-AI, interaction, HCI, homogenization, algorithmic monoculture, fragmentation, policy, AI safety, AI ethics, metascience, alignment, value alignment, pluralistic alignment, socioaffective, RLHF, reinforcement learning, psychology, persuasion, manipulation, political, influence, misuse, misalignment, incentives, institutions, knowledge commons, data commons, information environment

AI Epistemic Risks: Emerging Mechanisms & Evidence

Posted: 23 Jun 2026
Stephen Casper, Jonathan Stray, Anna Gausen, Cameron Jones, Brian Christian, Bálint Gyevnár, Siao Si Looi, Joshua Levy, Elizabeth Seger, Hannah Kirk, Kobi Hackenburg, Matt Kowal, Michelle ., Dan Zhao, Zhonghao He, Luke Hewitt, Hause Lin, Maarten Sap, Dylan Hadfield-Menell, Atoosa Kasirzadeh, Gordon Pennycook, Jean-François Godbout, Kellin Pelrine, Reihaneh Rabbany, Natasha Jacques, Jasmine Li, Yoshua Bengio, David Rand, Thomas Costello and Mick Yang
Massachusetts Institute of Technology (MIT), University of California, Berkeley - Center for Human-Compatible AI, Imperial College London, Stony Brook University, University of Oxford, Carnegie Mellon University, FAR.AI, FAR.AI, Tony Blair Institute for Global Change, University of Oxford, University of Oxford, FAR.AI, ILINA Program, Massachusetts Institute of Technology (MIT), University of Cambridge, Stanford University, Massachusetts Institute of Technology (MIT), Carnegie Mellon University, Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science, Carnegie Mellon University, Cornell University - Department of Psychology, University of Montreal - Université de Montreal, FAR.AI, McGill University, University of Washington, Cornell University, University of Montreal - Department of Informatics and Operations Research, Cornell University, Carnegie Mellon University and University of Pennsylvania

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AI, AI Risks, Epistemic Risks, Cognitive, Science, Influence, LLMs, Psychology, Education, Development, Learning, Education, AI-AI, Human-AI, Interaction, Feedback Loops, Echo Chambers, Homogenization, Monoculture, Fragmentation, Conflict, Lock-in, Value Alignment, Pluralistic Alignment, Technical Alignment, Moral Alignment, Incentives, Information Environment, AI Governance, AI Policies, Sycophancy, Manipulation, Persuasion, Socioaffective, Epistemic Resilience, AI Education, AI Literacy, Interaction Design