26 citations · 38 across the 18 of their papers we have counts for
19 papers
SafeGesture: Evaluating Fine-Grained Hand Gesture Understanding in Vision-Language Models through Scenario-Conditioned Safety Interpretation
Taegang Kim, Saleh Afroogh, Junfeng Jiao
Open-weight and frontier vision-language models (VLMs) perform well on general image understanding, but their ability to interpret fine-grained hand gestures in safety-critical ope…
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines
Saleh Afroogh, Yasser Pouresmaeil, Yiming Xu +3
Large Language Models (LLMs) are rapidly reshaping academic research across the natural sciences, social sciences, and humanities, yet the scientific community lacks a comprehensiv…
AI Empathy Erodes Cognitive Autonomy in Younger Users
Junfeng Jiao, Abhejay Murali, Saleh Afroogh
Affective alignment in generative AI represents a systemic risk to the developmental autonomy of younger users. Although emotional mirroring is commonly seen as a hallmark of advan…
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Saleh Afroogh, Syed Ishtiaque Ahmed, Petra Ahrweiler +46
This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs…
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…
Do You Feel Comfortable? Detecting Hidden Conversational Escalation in AI Chatbots
Jihyung Park, Saleh Afroogh, David Atkinson +1
Large Language Models (LLM) are increasingly integrated into everyday interactions, serving not only as information assistants but also as emotional companions. Even in the absence…
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…
Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Prediction
Yiming Xu, Junfeng Jiao
Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models are constrained by rigid assump…
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…
SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance
Junfeng Jiao, Jihyung Park, Yiming Xu +2
Despite the abundance of public safety documents and emergency protocols, most individuals remain ill-equipped to interpret and act on such information during crises. Traditional e…
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…
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…
Navigating LLM Ethics: Advancements, Challenges, and Future Directions
Junfeng Jiao, Saleh Afroogh, Yiming Xu +1
This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LL…