How to Deal with AI Hallucinating, Copyright, and Fact-Checking

The article highlights the critical tension between leveraging AI for media production and the risks of copyright infringement and factual inaccuracies stemming from unvetted public datasets. Experts emphasize that relying on open web data creates a "garbage in, garbage out" scenario, where AI models perpetuate errors and violate intellectual property rights. This necessitates a shift away from generic, uncontrolled training sources toward curated, proprietary data environments that ensure reliability and legal compliance in professional broadcasting and streaming contexts. Key solutions involve isolating generative components and utilizing licensed, rights-managed content rather than unrestricted internet scraping. By partnering with established stock libraries and implementing strict developer responsibilities for output accuracy, companies can mitigate hallucinations and copyright violations. This approach requires acknowledging that not all users have the same risk tolerance, prompting some platforms to offer modular tools that allow professionals to control how much AI influence is applied to scripts and visual assets. For open data advocates, this discourse is highly relevant as it underscores the urgent need for ethical data sourcing and transparency in AI training. It illustrates the dangers of the "open" internet when used as a raw training material without permission or context, advocating instead for controlled, domain-specific datasets. The conversation promotes a future where AI development prioritizes data ownership and specificity, challenging the prevailing model of indiscriminate data scraping and encouraging the creation of trusted, closed-loop data ecosystems for professional use.

Source: streamingmedia.com
Published on 2023-09-14

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