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
