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“Personality vs. Personalization” in AI Systems: Intersection with Evolving U.S. Law (Part 3)
This post is the third in a series on personality versus personality in AI systems. Read Part 1 (exploring concepts) and Part 2 (concrete uses and risks). Conversational AI technologies are hyper-personalizing. Across sectors, companies are focused on offering personalized experiences that are tailored to users’ preferences, behaviors, and virtual and physical environments. These […]
A Price to Pay: U.S. Lawmaker Efforts to Regulate Algorithmic and Data-Driven Pricing
“Algorithmic pricing,” “surveillance pricing,” “dynamic pricing”: in states across the U.S., lawmakers are introducing legislation to regulate a range of practices that use large amounts of data and algorithms to routinely inform decisions about the prices and products offered to consumers. These bills—targeting what this analysis collectively calls “data-driven pricing”—follow the Federal Trade Commission (FTC)’s […]
Tech to Support Older Adults and Caregivers: Five Privacy Questions for Age Tech
[…] narrowly on specific types of health data individually or in tandem with AI or other technologies. For example, mental health chatbots (Utah HB452), reproductive health data ( Virginia SB754), or AI disclosures in clinical settings (California AB3030). Other bills and laws have broad definitions that include location, movement, and voice data, all common types […]
Meet Bianca-Ioana Marcu, FPF Europe Managing Director
FPF is pleased to welcome our colleague Bianca-Ioana Marcu to her new role as Managing Director of FPF Europe. With extensive experience in privacy and data protection, she takes on this responsibility at a pivotal moment for digital regulation in Europe. In this blog, we will explore her perspectives on the evolving privacy landscape, her […]
FPF Unveils Paper on State Data Minimization Trends
Today, the Future of Privacy Forum (FPF) published a new paper—Data Minimization’s Substantive Turn: Key Questions & Operational Challenges Posed by New State Privacy Legislation. Data minimization is a bedrock principle of privacy and data protection law, with origins in the Fair Information Practice Principles (FIPPs) and the Privacy Act of 1974. At a high […]
DBJ_Weld_Re-Identification
[…] we should actively prohibit re-identification, and require those with access to de-identified data to guard and use it appropriately. HHS Office of Civil Rights (OCR) regulators have promised to provide new guidance in the near future for the de-identification of health data in response to a Congressional mandate to do so. HHS OCR regulators […]
The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
[…] and privacy budget management. Errors in implementation, such as underestimating sensitivity or mismanaging the privacy budget across multiple queries (due to composition rules), can silently undermine the promised privacy guarantees. Defining the “privacy unit” (e.g., user, query, session) appropriately is critical; misclassification can lead to unintended disclosures. Auditing DP implementations for correctness is also […]
FPF Launches Major Initiative to Study Economic and Policy Implications of AgeTech
FPF and University of Arizona Eller College of Management Awarded Grant by Alfred P. Sloan Foundation to Address Privacy Implications, and Data Uses of Technologies Aimed at Aging At Home The Future of Privacy Forum (FPF) — a global non-profit focused on data protection, AI and emerging technologies–has been awarded a grant from the Alfred […]