8 citations · 19 across the 11 of their papers we have counts for
13 papers
Identifying Introspection From the Inside
David I. Atkinson, Dillon Plunkett, David Bau
Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we distinguish plausible confabulat…
Government AI Use as a Monitoring Primitive: A Public Document Pilot Study
David I. Atkinson, Joan Eleanor O'Bryan
Governments are important actors in frontier AI governance, but many facts about their adoption and use of AI systems are difficult to observe directly. Procurement disclosures and…
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…
Evaluating the Effectiveness of OpenAI's Parental Control System
Kerem Ersoz, Saleh Afroogh, David Atkinson +1
We evaluate how effectively platform-level parental controls moderate a mainstream conversational assistant used by minors. Our two-phase protocol first builds a category-balanced…
LLM Harms: A Taxonomy and Discussion
Kevin Chen, Saleh Afroogh, Abhejay Murali +3
This study addresses categories of harm surrounding Large Language Models (LLMs) in the field of artificial intelligence. It addresses five categories of harms addressed before, du…
Evaluating LLM Safety Across Child Development Stages: A Simulated Agent Approach
Abhejay Murali, Saleh Afroogh, Kevin Chen +3
Current safety alignment for Large Language Models (LLMs) implicitly optimizes for a "modal adult user," leaving models vulnerable to distributional shifts in user cognition. We pr…
Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions
Junfeng Jiao, Saleh Afroogh, Kevin Chen +3
As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical impera…
LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models
Junfeng Jiao, Saleh Afroogh, Abhejay Murali +3
This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs) as they increasingly integrate into critical…
LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction
Junfeng Jiao, Saleh Afroogh, Kevin Chen +3
Large Language Models (LLMs) are increasingly embedded in child-facing contexts such as education, companionship, creative tools, but their deployment raises safety, privacy, devel…
AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models
Junfeng Jiao, Saleh Afroogh, Kevin Chen +2
This study introduces AGGA, a dataset comprising 80 academic guidelines for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in academic settings, meticulously col…
IGGA: A Dataset of Industrial Guidelines and Policy Statements for Generative AIs
Junfeng Jiao, Saleh Afroogh, Kevin Chen +2
This paper introduces IGGA, a dataset of 160 industry guidelines and policy statements for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in industry and workpla…
Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors
Junfeng Jiao, Saleh Afroogh, Kevin Chen +2
The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while rai…
The global landscape of academic guidelines for generative AI and Large Language Models
Junfeng Jiao, Saleh Afroogh, Kevin Chen +2
The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and…