Surveys

Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports.

OBJECTIVES: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.

Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs

Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, such as Retrieval Augmented Generation, partially address these issues by grounding answers in source documents, but hallucinations and low fact-level explainability persist. In thi...

Redefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins

Crowd work platforms like Amazon Mechanical Turk and Prolific are vital for research, yet workers' growing use of generative AI tools poses challeng...

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models

Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully...

PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation

In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue parti...

Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images

The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) an...

How Does Response Length Affect Long-Form Factuality

Large language models (LLMs) are widely used for long-form text generation. However, factual errors in the responses would undermine their reliabili...

Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models (LLMs) by conditioning generation on extern...

VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models

While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less atten...

Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds

Mental disorders including depression, anxiety, and other neurological disorders pose a significant global challenge, particularly among individuals...

A Survey on Training-free Open-Vocabulary Semantic Segmentation

Semantic segmentation is one of the most fundamental tasks in image understanding with a long history of research, and subsequently a myriad of diff...

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data

Mixed methods research integrates quantitative and qualitative data but faces challenges in aligning their distinct structures, particularly in exam...

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy

AI copilots, context-aware, AI-powered systems designed to assist users in tasks such as software development and content creation, are becoming int...

STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds

In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures;...

Beyond Explainability: The Case for AI Validation

Artificial Knowledge (AK) systems are transforming decision-making across critical domains such as healthcare, finance, and criminal justice. Howeve...

Creativity in LLM-based Multi-Agent Systems: A Survey

Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While ...

Interpreting Social Bias in LVLMs via Information Flow Analysis and Multi-Round Dialogue Evaluation

Large Vision Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet they also exhibit notable social biases. These biase...

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions

Bio-inspired algorithms (BIAs) utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, ...

Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey

Video-based person re-identification (Re-ID) remains brittle in real-world deployments despite impressive benchmark performance. Most existing model...

Deep Spectral Prior

We introduce Deep Spectral Prior (DSP), a new formulation of Deep Image Prior (DIP) that redefines image reconstruction as a frequency-domain alignm...