26 citations · 53 across the 23 of their papers we have counts for
10 papers · 1 filter
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…
A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge
Saleh Afroogh, Kush R. Varshney, Jason D'Cruz
According to several empirical investigations, despite enhancing human capabilities, human-AI cooperation frequently falls short of expectations and fails to reach true synergy. We…
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…
Mapping out AI Functions in Intelligent Disaster (Mis)Management and AI-Caused Disasters
Yasser Pouresmaeil, Saleh Afroogh, Junfeng Jiao
This study maps the functions of artificial intelligence in disaster (mis)management. It begins with a classification of disasters in terms of their causal parameters, introducing…
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…