Cultural Competence

Large Language Models Generate Stigmatizing Language During Reasoning Over Real-World Clinical Data

Stigmatizing language in clinical documentation, which conveys negative stereotypes, attitudes, or judgments toward patients, is a recognized source of documentation bias and is associated with poorer care and adverse health outcomes. Although prior stigma-related research has focused on clinician-written EHR notes, the increasing use of large language model (LLM)-generated documentation in clinic...

Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements

Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegi...

MASCOT: Model-Aware Submodular Coverage for Composite-Attribute Text-to-Image Retrieval

Vision-Language Models (VLMs) are highly effective in retrieving semantically relevant images. However, in practice, relevance alone is often insuffic...

Through Van Gogh's Eyes: Global Style Transfer with Diffusion Mod

Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's gl...

Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification

Low-light imaging often introduces color bias caused by the low signal-to-noise ratio and the image formation process. Although recent low-light image...

Self-Evolving Code-with-Image Reasoning

Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The...

Agentic Visual Reasoning in Whole-Slide Pathology Images via Active Perception

Whole-slide visual reasoning requires identifying sparse diagnostic evidence in gigapixel pathology slides and integrating observations across spatial...

Agentic Anomaly Detection with ORCA-Style Dynamic Inductive Bias Adaptation in Multimodal Wearable Time Series Data

Wireless Body Area Networks (WBANs) generate multivariate physiological time series that are highly nonstationary and must often be processed under st...

Deep learning-guided identification of bacteriophage receptor-binding protein candidates for foodborne pathogen detection

Foodborne pathogens including Salmonella spp., Escherichia coli and Listeria monocytogenes cause an estimated 600 million illnesses annually. Yet conv...

When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins

Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fi...

Investigating Social Bias in Narrative Image Generation

Text-to-image (T2I) generation models are increasingly embedded in applications such as media content creation and education, raising concerns about h...

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital he...

How Bias Shapes the Leaderboard: Scoring Function Performance Under Scrutiny

Structure-based scoring functions leveraging machine learning have recently demonstrated superior performance over classical scoring functions, partic...

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly un...

Forty-seven percent of pregnancies have stigmatizing language in their clinical notes in an electronic health record cohort

Stigmatizing language in medical documentation may reflect and perpetuate bias, but its prevalence in obstetrics has not been systematically quantifie...

Parallel Decoding Distillation for Fast Image and Video Generation

Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SO...

Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling

Few-step distilled diffusion models generate high-quality images quickly, but often lose per-prompt diversity, producing near-identical samples across...

Adversarial Validation Reveals Diagnostic Workflow Leakage in PCOS Machine Learning Models

Background: Machine-learning models for polycystic ovary syndrome (PCOS) and other conditions frequently report near-perfect diagnostic performance, b...

Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferrin...

Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias

Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applic...