Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At an international conference in 2022, experts gathered to discuss strategies for reducing bias in he...
BACKGROUND: Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among peop...
Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stab...
Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically s...
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising...
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
39694331
As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny. Ethics and bias are important considerations in our practice set...
We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI ...
Journal of chemical information and modeling
39681303
It stands to reason that the amount and the quality of data are of key importance for setting up accurate artificial intelligence (AI)-driven models. Among others, a fundamental aspect to consider is the bias introduced during sample selection in dat...
BACKGROUND: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this kn...
Journal of the American Medical Informatics Association : JAMIA
39569464
OBJECTIVES: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).