Webinar outlines tools and practices to reduce AI bias

Gadeau closed the webinar with mitigation strategies attendees can adopt. He recommended inclusive LLMs (citing Latimer as an example), running trusted models on curated local data, auditing sources with tools like NotebookLM and using artist protections such as Nightshade to block content from being easily ingested by crawlers.

He offered four practical guidelines: co‑create rather than let AI decide, avoid placing personal data into prompts, keep an eye out for bias and treat AI outputs as starting points for human review. "You can always be skeptical. That's the number 1 thing you can do," Gadeau said, and he urged participants to apply gut checks and verification when reusing model outputs.