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HuggingFace 101: Hidden Dangers of Open Source Models

Hosted by Jai Bhagat

Wed, Oct 7, 2026

4:00 PM UTC (45 minutes)

Virtual (Zoom)

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Make Safer AI Decisions
Jai Bhagat and Nicole Mercede
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What you'll learn

Spot hidden risk surfaces

Identify where licensing, data provenance, model behavior, and deployment assumptions can create unexpected exposure.

Ask better evaluation questions

Separate broad benchmark claims from the checks your own use case needs before adoption.

Choose safer next steps

Use a lightweight review checklist to decide whether to test, limit, or avoid an open-source model.

Why this topic matters

Open-source models can be useful and practical, but the risks are often hidden in licensing, data provenance, deployment defaults, and evaluation gaps. In this lightning lesson, we will look at one product decision, inspect the evidence around the model, and decide what to ask before a team adopts it.

You'll learn from

Jai Bhagat

Director of AI Education at A+ Active

Teaching is the love of my life. I've taught meditation classes at Madison Square Garden, taught systems at Parsons School of Design and now I livestream myself learning to write kernels and advanced programming for GPUs and AI workloads.

Jai believes that with love and imagination that anything is possible.

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