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FPF-Comment-Letter-Colorado-Minor-Privacy-Protections-Rulemaking_9.10.25
[…] d onlin e sa fe ty wit h au to no m y an d a cce ss, we writ e ad dre ssin g tw o parts of th e ru le m akin g fo r th e Dep artm ent’s co nsid era tio n: ● Fir s t, th e […]
“Personality vs. Personalization” in AI Systems: Responsible Design and Risk Management (Part 4)
This post is the fourth and final blog post in a series on personality versus personalization in AI systems. Read Part 1 (exploring concepts), Part 2 (concrete uses and risks), and Part 3 (intersection with U.S. law). Conversational AI technologies are hyper-personalizing. Across sectors, companies are focused on offering personalized experiences that are tailored to […]
“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 […]
“Personality vs. Personalization” in AI Systems: Specific Uses and Concrete Risks (Part 2)
[…] populations (e.g., minors, many of whom have used AI companions) may be particularly susceptible to this risk due to their level of cognitive development and mental states. Amplification of biases and filter bubbles: Users may impart their biases to AI companions and chatbots, which, in an effort to customize experiences, may emulate these world […]
“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 […]
Highlights from FPF’s July 2025 Technologist Roundtable: AI Unlearning and Technical Guardrails
[…] goal at hand, different technical guardrails and means of operationalizing “unlearning” have different levels of effectiveness. In this post-event summary, we highlight the key takeaways from three parts of the Roundtable on July 17: Machine Unlearning: Overview and Policy Considerations Core “Unlearning” Methods: Exact vs. Approximate Technical Guardrails and Risk Mitigation If you have […]
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 […]
The “Neural Data” Goldilocks Problem: Defining “Neural Data” in U.S. State Privacy Laws
Co-authored by Chris Victory, FPF Intern As of halfway through 2025, four U.S. states have enacted laws regarding “neural data” or “neurotechnology data.” These laws, all of which amend existing state privacy laws, signify growing lawmaker interest in regulating what’s being considered a distinct, particularly sensitive kind of data: information about people’s thoughts, feelings, and […]