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The Shibboleth Rule for Artificial Agents
Bots could one day dispense medical advice, teach our children, or call to collect debt. How can we avoid being deceived...
Using AI To Personalize Cancer Care
Stanford scholars have developed an algorithm that identifies the best treatments for various subtypes of cancer.
How Flawed Data Aggravates Inequality in Credit
AI offers new tools for calculating credit risk. But it can be tripped up by noisy data, leading to disadvantages for...
The Open-Source Movement Comes to Medical Datasets
Hoping to spur crowd-sourced AI applications in health care, Stanford’s AIMI center is expanding its free repository of...
First-of-its-kind Stanford Machine Learning Tool Streamlines Student Feedback Process for Computer Science Professors
Stanford professors develop and use an AI teaching tool that can provide feedback on students’ homework assignments in...
How Artificial Neural Networks Help Us Understand Neural Networks in the Human Brain
Experts from psychology, neuroscience, and AI settle a seemingly intractable historical debate in neuroscience — opening...
Rooting Out Anti-Muslim Bias in Popular Language Model GPT-3
This “severe” bias must be addressed before these language models become ingrained in real-world tasks.
De-Identifying Medical Patient Data Doesn’t Protect Our Privacy
A Stanford researcher makes the case that de-identifying health records used for research doesn’t offer anonymity and...
Why AI Struggles To Recognize Toxic Speech on Social Media
AI speech police are smart and fast, so why is there a gap between strong algorithmic performance and reality?
Using Artificial Intelligence to Understand Why Students are Struggling
Stanford researchers created a program to help when students get stuck in self-paced digital learning.
Future of Work: Beyond Bossware and Job-Killing Robots
To encourage a human-centered workplace, we must rethink AI-driven automation, bossware, labor taxes, and corporate R...
A Moderate Proposal for Radically Better AI-powered Web Search
Large language models could give us instant answers, but at a cost to trust. Stanford scholars propose an alternative.