EmTech Digital 2024: Exploring the privacy problems of generative AI

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Patricia Thaine, cofounder and CEO of Private AI, discusses the potential problem spots that could arise when the outputs of LLMs divulge sensitive data or other types of personally identifiable information

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Private AI works on a platform that allows organizations to embed a specific privacy tool that it might need to use in its product of software pipeline while respecting privacy laws and minimizing the possibility of privacy leaks.

At the very least, she said regulatory compliance with privacy in AI can be done by design – or at the beginning of work on the AI – rather than having what she called “a band-aid solution at the end.”Speaking with James O’Donnell, MIT Technology Review’s reporter on AI, Thaine also discussed privacy corner cases and PIIs as it related to the work of Private AI.

Thaine also discussed Private AI’s coordination with partners, such as highly-regulated industries or companies aiming to innovate quickly, to determine specific corner cases where privacy can be strengthened for a given particular situation, and then generate synthetic or fake information to train their systems to not divulge information in a given case.

 

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