Explainable Artificial Intelligence (XAI) seeks to render the operation and decisions of complex machine learning systems transparent and interpretable to users, regulators and other stakeholders. As ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

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 ...

A practical review of explainable AI examines how transparency and interpretability improve trust in high-stakes applications. By introducing ...

The hydrologic system is subjected unprecedented stresses and increasing demands driven by climate variabilities, landuse changes, groundwater ...

You will be redirected to our submission process. The accelerating pace of global environmental change driven by climate change, biodiversity loss, land degradation, and urbanization demands ...