One of the many ethical issues around the advancement of generative AI relates to detecting the product of models, writes Stephanie Kirmer.
“When we talk about content being difficult or impossible to detect as AI generated, we’re getting into something akin to a Turing Test. … If I ask you, “was this produced by a human being or a machine learning model?”, and you can’t accurately tell, then we’ve reached the point where we need to think about these issues,” Kirmer says.
Efforts from organizations such as the Content Authenticity Initiative may help identify AI-generated content, she notes. And, “we can use models as tools on our side of the race as well, training models to scrutinize content and determine that it isn’t human generated.”
In this article, Kirmer explains the importance of authentication and describes related efforts, such as the U.S. Executive Order. Ultimately, there’s no easy answer to this problem, she says, but it’s important to have the discussion and to take responsibility “for spreading accurate and accessible information about these issues.”
Read more at Towards Data Science.
- Detect DeepFakes: How to counteract misinformation created by AI — MIT Media Lab
- The Environmental Impact of AI — FOSSlife
- Facebook Is Being Overrun With Stolen, AI-Generated Images That People Think Are Real — 404 Media
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