When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...
A leakage-aware evaluation of explainable machine learning found modest prediction of nanoparticle tumor delivery, with ...
A new study combines LSTM-based deep learning with LIME and SHAP explainability to estimate EV battery state of health across ...
AI systems have tremendous potential, but the average user has little visibility and knowledge on how the machines make their decisions. AI explainability can build trust and further push the ...
Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...
Krishna, Satyapriya, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Transactions on ...
When large volumes of text are grouped into similar topics, being able to explain why they were grouped that way is useful ...
It is 22:00 again. To all data scientists, machine learning engineers, and business professionals interested in AI, we have ...
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