Cultural Competence

Revisiting Technical Bias Mitigation Strategies

Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions affecting their real-world implementation...

Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning

Deep neural networks (DNNs) suffer from the spectral bias, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency components of a function, struggling to capture its high-frequency features. This paper is to address this issue. Notice that a function having only low frequency components may be well-represented by a shallow neural network (SNN), a network having o...

Text-to-Image Representativity Fairness Evaluation Framework

Text-to-Image generative systems are progressing rapidly to be a source of advertisement and media and could soon serve as image searches or artists...

Personas with Attitudes: Controlling LLMs for Diverse Data Annotation

We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investiga...

Diversity-Aware Reinforcement Learning for de novo Drug Design

Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often f...

What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias

Knowledge Distillation is a commonly used Deep Neural Network (DNN) compression method, which often maintains overall generalization performance. Ho...

Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models

Text-To-Image (TTI) Diffusion Models such as DALL-E and Stable Diffusion are capable of generating images from text prompts. However, they have been...

An Effective Theory of Bias Amplification

Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To better unders...

Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach

Objective: To improve prediction of Chronic Kidney Disease (CKD) progression to End Stage Renal Disease (ESRD) using machine learning (ML) and deep ...

Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning

Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel d...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it en...

A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research

Recommender systems are essential for personalizing digital experiences on e-commerce sites, streaming services, and social media platforms. While t...

Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models

With the growing adoption of Text-to-Image (TTI) systems, the social biases of these models have come under increased scrutiny. Herein we conduct a ...

OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System

Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) c...

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithm...

Lookism: The overlooked bias in computer vision

In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and ge...

Gender Bias Evaluation in Text-to-image Generation: A Survey

The rapid development of text-to-image generation has brought rising ethical considerations, especially regarding gender bias. Given a text prompt a...

An Efficient and Explanatory Image and Text Clustering System with Multimodal Autoencoder Architecture

We demonstrate the efficiencies and explanatory abilities of extensions to the common tools of Autoencoders and LLM interpreters, in the novel conte...

Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games

Defining and measuring decision-making styles, also known as playstyles, is crucial in gaming, where these styles reflect a broad spectrum of indivi...

Civiverse: A Dataset for Analyzing User Engagement with Open-Source Text-to-Image Models

Text-to-image (TTI) systems, particularly those utilizing open-source frameworks, have become increasingly prevalent in the production of Artificial...