A practical review of explainable AI examines how transparency and interpretability improve trust in high-stakes applications. By introducing ...
Evan Hackstadt is a computer science major with minors in biology and math. He is a 2025-26 health care ethics intern at the Markkula Center for Applied Ethics at Santa Clara University. Views are his ...
In my previous article, I discussed the importance of AI explainability and the different categories of AI explainability, explainable predictions, explainable algorithms and interpretable ...
As the impact of artificial intelligence (AI) grows in our world, the University of Adelaide is exploring the role that technology can play in the health sphere, particularly in clinical ...
Explainable AI provides human users with tools to understand the output of machine learning algorithms. One of these tools, feature attributions, enables users to know the contribution of each feature ...
One of the most important aspects of data science is building trust. This is especially true when you're working with machine learning and AI technologies, which are new and unfamiliar to many people.
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Machine learning (ML) approaches are being proposed more frequently for predictive modelling in medicine and healthcare, and have the potential to revolutionize medicine 1. However, ML methods have ...
Traditional materials science studies depend heavily on the knowledge of individual experts. Expert knowledge is highly useful, especially for advancing physical understanding and generating new ...
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