The global demand for Explainable AI (XAI) is accelerating as organizations seek greater transparency and accountability in artificial intelligence systems used across critical industries. According ...

Python and statistics still sit at the center of data science, but the work surrounding them has expanded. Professionals now move from cleaning data and testing hypotheses into predictive modeling, ...

Building and scaling AI with trust and transparency is crucial for any organization. For explainable AI (XAI) to be effective, it must enable transparency, explain the predictions and algorithm and ...

As artificial intelligence usage continues to increase, there’s a problem lurking in the background growing larger by the day: It’s the ability of AI to explain itself so it’s clear what led to an ...

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, ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

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 ...

SALT LAKE CITY, UTAH – Researchers at the University of Utah's Department of Psychiatry and Huntsman Mental Health Institute today published a paper introducing RiskPath, an open source software ...

The researchers examined the Open University Learning Analytics Dataset, known as OULAD, and EdNet to determine which could support a meaningful picture of exploratory behavior.