25 citations · 107 across the 30 of their papers we have counts for
35 papers
Pseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
Pronob Kumar Barman, Tera L. Reynolds, James Foulds
Online Health Communities connect patients for peer support, but users face a discovery challenge when they have minimal prior interactions to guide personalization. We study recom…
Enhancing Online Support Group Formation Using Topic Modeling Techniques
Pronob Kumar Barman, Tera L. Reynolds, James Foulds
Online health communities (OHCs) are vital for fostering peer support and improving health outcomes. Support groups within these platforms can provide more personalized and cohesiv…
Understanding User Perceptions of Human-centered AI-Enhanced Support Group Formation in Online Healthcare Communities
Pronob Kumar Barman, James R. Foulds, Tera L. Reynolds
Peer support is critical to managing chronic health conditions. Online health communities (OHCs) enable patients and caregivers to connect with similar others, yet their large scal…
A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City
Samin Semsar, Kiran Laxmikant Prabhu, Gabriella Waters +1
There are ongoing discussions about predictive policing systems, such as those deployed in Los Angeles, California and Baltimore, Maryland, being unfair, for example, by exhibiting…
A Unifying Human-Centered AI Fairness Framework
Munshi Mahbubur Rahman, Shimei Pan, James R. Foulds
The increasing use of Artificial Intelligence (AI) in critical societal domains has amplified concerns about fairness, particularly regarding unequal treatment across sensitive att…
You've Changed: Detecting Modification of Black-Box Large Language Models
Alden Dima, James Foulds, Shimei Pan +1
Large Language Models (LLMs) are often provided as a service via an API, making it challenging for developers to detect changes in their behavior. We present an approach to monitor…
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
Siyuan Wang, James R. Foulds, Md Osman Gani +1
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corrobo…
Can Generative AI be Egalitarian?
Philip Feldman, James R. Foulds, Shimei Pan
The recent explosion of "foundation" generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This…
ALDAS: Audio-Linguistic Data Augmentation for Spoofed Audio Detection
Zahra Khanjani, Christine Mallinson, James Foulds +1
Spoofed audio, i.e. audio that is manipulated or AI-generated deepfake audio, is difficult to detect when only using acoustic features. Some recent innovative work involving AI-spo…
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
Philip Feldman, James R. Foulds, Shimei Pan
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false inf…
Killer Apps: Low-Speed, Large-Scale AI Weapons
Philip Feldman, Aaron Dant, James R. Foulds
The accelerating advancements in Artificial Intelligence (AI) and Machine Learning (ML), highlighted by the development of cutting-edge Generative Pre-trained Transformer (GPT) mod…
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings
Fatema Hasan, Yulong Li, James Foulds +2
Speech data has rich acoustic and paralinguistic information with important cues for understanding a speaker's tone, emotion, and intent, yet traditional large language models such…
Trapping LLM Hallucinations Using Tagged Context Prompts
Philip Feldman, James R. Foulds, Shimei Pan
Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from "hallucinations," where th…
The Keyword Explorer Suite: A Toolkit for Understanding Online Populations
Philip Feldman, Shimei Pan, James R. Foulds
We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pip…
Fair Inference for Discrete Latent Variable Models
Rashidul Islam, Shimei Pan, James R. Foulds
It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional…
Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction
George J. Cancro, Shimei Pan, James Foulds
When a human receives a prediction or recommended course of action from an intelligent agent, what additional information, beyond the prediction or recommendation itself, does the…
Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models
Philip Feldman, Aaron Dant, James R. Foulds +1
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research…
Learning User Embeddings from Temporal Social Media Data: A Survey
Fatema Hasan, Kevin S. Xu, James R. Foulds +1
User-generated data on social media contain rich information about who we are, what we like and how we make decisions. In this paper, we survey representative work on learning a co…
Analyzing COVID-19 Tweets with Transformer-based Language Models
Philip Feldman, Sim Tiwari, Charissa S. L. Cheah +2
This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT model…
Fair Representation Learning for Heterogeneous Information Networks
Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya +3
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and pro…
Equitable Allocation of Healthcare Resources with Fair Cox Models
Kamrun Naher Keya, Rashidul Islam, Shimei Pan +2
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most lang…
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification
Guohou Shan, James Foulds, Shimei Pan
Text features that are correlated with class labels, but do not directly cause them, are sometimesuseful for prediction, but they may not be insightful. As an alternative to tradit…
Neural Fair Collaborative Filtering
Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng +2
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure…