# Building Better AI While Preserving User Privacy With TripleBlind

Page: https://stenobird.com/podcast/ai-engineering-podcast/building-better-ai-while-preserving-user-privacy-with-tripleblind
Text version: https://stenobird.com/podcast/ai-engineering-podcast/building-better-ai-while-preserving-user-privacy-with-tripleblind.md
Podcast: [AI Engineering Podcast](https://stenobird.com/podcast/ai-engineering-podcast)
Published: 2023-11-22T01:00:00+00:00
Episode link: https://www.aiengineeringpodcast.com/tripleblind-ai-user-privacy-episode-25
Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/6385305386858341222aeb9164-6321-4df4-8945-60130f053694v2.mp3
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JSON: https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/building-better-ai-while-preserving-user-privacy-with-tripleblind
Duration seconds: 2814

## Resource

Summary Machine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the current generation of applications can be susceptible to leaking user data and how to counteract those trends. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Gharib Gharibi about the challenges of bias and data privacy in generative AI models Interview Introduction How did you get involved in machine learning? Generative AI has been gaining a lot of attention and speculation about its impact. What are some of the risks that these capabilities pose?  What are the main contributing factors to their existing shortcomings? What are some of the subtle ways that bias in the source data can manifest? In addition to inaccurate results, there is also a question of how user interactions might be re-purposed and potential impacts on data and personal privacy. What are the main sources of risk? With the massive attention that generative AI has created and the perspectives that are being shaped by it, how do you see that impacting the general perception of other implementations of AI/ML?  How can ML practitioners improve and convey the trustworthiness of their models to end users? What are the risks for the industry if generative models fall out of favor with the public? How does your work at Tripleblind help to encourage a conscientious a…

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