AI Training Videos

An accessible look at the "black box" in AI: what it means, why it's risky in real-world settings (healthcare, driving, criminal justice), and how explainable AI aims to fix it.

A plain‑language look at AI "hallucinations": why large language models can confidently give false answers, how knowledge cutoffs and missing context cause those errors, and simple ways to ground AIs so they're more accurate.

Practical guidance for library staff on protecting patron and organizational data when using AI tools.

A plain-language overview of how AI is trained on human-created work, why that raises copyright disputes, and what's currently settled (and unsettled) about who owns AI-generated content.

A close look at how pricing and infrastructure can limit who benefits from AI, and what communities and libraries can do to help.

An introductory review of how AI can reflect human bias (from image tools to hiring and criminal justice), real-world examples, and practical steps libraries can take to understand and respond.

A short primer on how AI-driven automation can replace jobs, real-world examples of when that went wrong, which roles are most at risk, and how libraries and community organizations can respond.

A realistic look at where AI lives, why it consumes large amounts of electricity and water, and what that means for communities and policymakers.