Making Generative AI Transparent

In this episode of ⁠Data Security Decoded⁠, host ⁠Caleb Tolin⁠ sits down with ⁠Gabrielle Hibbert⁠, a social policy expert and researcher, about her innovative work developing a nutrition labeling system for generative AI tools. This framework aims to bridge the gap between complex AI technology and consumer understanding, while addressing critical transparency and data privacy concerns. What You'll Learn: How nutrition labels for AI tools can make complex technology accessible to non-technical users Why current privacy policies fail to protect consumers, with 93% of users unable to understand them The three-pillar approach to AI transparency: general usage information, safety measures, and potential risks How companies can balance corporate sensitivity with consumer transparency in AI tool deployment Why Generation Z and Millennial users feel increasingly burdened by technology, and how transparency can help The regulatory framework needed to standardize AI tool labeling across industries How iterative processes and APIs can keep AI nutrition labels current with rapid technological changes The importance of multi-stakeholder collaboration in developing effective AI transparency standards Episode Highlights: [00:00:55] Creating Consumer-Friendly AI Transparency Labels [04:58] Building Universal Understanding Across Technical Levels [22:13] Regulatory Framework Integration [27:21] Dynamic Updates Through API Integration Episode Resources: Caleb Tolin on LinkedIn Gabrielle Hibbert on LinkedIn FCC Broadband Labeling System New America – Translating the Artificial Report Page FDA Nutrition Label Design Standards

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