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Comparison of COPPA 2.0
[…] case, the operator is deemed to have collected the information); and 2 (B) does not include any organization described in section 501(c)(3) of the Internal Rev enue Code of 1986 and exempt from taxation under section 501(a) of such Code . nonprofit entity that would otherwise be exempt from coverage under section 45 of […]
FPF ANPR Comment 10 17_submitted
[…] FPF is a global non -profit organization dedicated to advancing privacy leadership, scholarship, and principled data practices in support of emerging technologies .2 FPF focuse s on promoting responsible data practices and has deep expertise regarding privacy and data protection, particularly concerning open banking. In addition to its traditional expertise, FPF offer s a […]
Rethinking Personal Data: The CJEU’s Contextual Turn in EDPS vs. SRB
[…] and ownership of Banco Popular instruments. Those deemed eligible could then submit comments through an online form. More than 23,000 comments were received, each assigned an alphanumeric code. In June 2019, the SRB transferred 1,104 comments relevant to the valuation to Deloitte via a secure server. Deloitte never received the underlying identification data or […]
FPF_CCPA Regulations Issue Brief
[…] Chart: Risk Assessment (DPIA) Requirements in California, Colorado, and the EU California Colorado EU FPF Analysis: CA v. CO References California Consumer Privacy Act (CCPA) Cal. Civ. Code § 1798.185, subd. (a)(15) Colorado Privacy Act (CPA) Colo. Rev. Stat. § 6-1-1309 General Data Protection Regulation (GDPR) Article 35 This comparison chart focuses on the […]
The State of State AI 2025 SUPPLEMENTAL
[…] Senior Director of U.S. Legislation, Future of Privacy Forum AUTHORS Thanks to Bridget Egan for her research contributions. ACKNOWLEDGEMENTS All FPF materials that are released publicly are free to share and adapt with appropriate attribution. Learn more . 3 Table 1. Legislative Outcomes for State AI Bills Overview of the 210 industry-focused AI bills […]
The State of State AI 2025
[…] Beyond disclosure and safety, some bills experimented with accountability measures tied to privacy and advertising . Utah’s SB 452 (enacted), for example, prohibits mental health chatbots from promoting products during conversations unless clearly labeled as advertising. California’s AB 1064 (enrolled) initially highlighted concerns about how personal data from chatbot interactions w ith youth may […]
“Personality vs. Personalization” in AI Systems: Responsible Design and Risk Management (Part 4)
[…] including subscription-based and enterprise pricing models. As personalized AI systems increasingly replace, or are integrated into, online search, they will impact online content that has largely been free and ad-supported since the early Internet. However, it is not clear that personalized AI systems can, or should, adopt compensation strategies that follow the same historical […]
“Personality vs. Personalization” in AI Systems: Intersection with Evolving U.S. Law (Part 3)
[…] through user-generated input. For example, a 2015 claim against Snap, Inc. survived Section 230 dismissal following a claim that a specific “Speed Filter” Snapchat feature (since discontinued) promoted reckless driving. In other cases, the personalization of a system through demographic-based targeting that causes harm may also implicate tort and product liability law when organizations […]
“Personality vs. Personalization” in AI Systems: Specific Uses and Concrete Risks (Part 2)
[…] personality-like features, whether it is a specific voice mode, or a consistent persona, or even a range of “AI companions.” Even if companion-like personalities are not directly promoted as features, users can build them using system prompts and customized design; an early 2023 feature of OpenAI enabled users to create custom GPTs. Figure 3 […]
“Personality vs. Personalization” in AI Systems: An Introduction (Part 1)
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 range from general purpose LLMs, to the rapidly growing market for LLM-powered AI companions, educational aides, and corporate assistants. There are clear trends among this overall focus: towards […]