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“Personality vs. Personalization” in AI Systems: An Introduction (Part 1)
[…] and be positive, upbeat and motivating.” Gemini will take those instructions and, with one click, enhance them to create a Gem that meets your specific needs.” “ Get more done with Gemini: Try 1.5 Pro and more intelligent features,” May 14, 2024, Google Meta “You can tell Meta AI to remember certain things about […]

AI Regulation in Latin America: Overview and Emerging Trends in Key Proposals
[…] the OECD’s AI Principles, focused on transparency, security, and responsibility of AI systems, and to UNESCO’s AI Ethics Recommendation, which emphasizes the need for a human-centered approach, promoting social justice and environmental sustainability in AI systems. All bills reviewed ground the development of AI in privacy or data protection as a guiding principle to […]

The “Neural Data” Goldilocks Problem: Defining “Neural Data” in U.S. State Privacy Laws
[…] by neurotechnologies, and excludes nonneural information, the other laws may implicitly do so as well. The Goldilocks Problem: The nature of “neural data” makes it challenging to get the definition just right. Given that each state law defines neural data differently, there may be significant variance in what kinds of data are covered. Generally, […]

The Connecticut Data Privacy Act Gets an Overhaul (Again)
[…] as they approach adulthood. For example, some career or scholarship quizzes may rely on profiling to tailor opportunities to a teen’s interests. * * * Looking to get up to speed on the existing state comprehensive consumer privacy laws? Check out FPF’s 2024 report, Anatomy of State Comprehensive Privacy Law: Surveying the State Privacy […]

Brazil’s ANPD Preliminary Study on Generative AI highlights the dual nature of data protection law: balancing rights with technological innovation
[…] In some instances, users may not be aware of the risks involved in sharing personal information or, if aware, they might choose to “trust the system” to get the answers and assistance they need. In this scenario, the CGTP points out that safeguards should be developed to create privacy-friendly systems. One way to achieve […]

Vermont and Nebraska: Diverging Experiments in State Age-Appropriate Design Codes
In May 2025, Nebraska and Vermont passed Age-Appropriate Design Code Acts (AADCs), continuing the bipartisan trend of states advancing protections for youth online. While these new bills arrived within the same week and share both a common name and general purpose, their scope, applicability, and substance take two very different approaches to a common […]

Amendments to the Montana Consumer Data Privacy Act Bring Big Changes to Big Sky Country
[…] lowest numerical applicability thresholds of any of the state comprehensive privacy laws when the law was enacted in 2023. At that time, prior comprehensive privacy laws in Virginia, Colorado, Utah, Connecticut, Iowa, and Indiana all applied to controllers that either (1) control or process the personal data of at least 100,000 consumers (“the general […]

The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
[…] values are replaced with broader, less precise categories. Examples include replacing an exact birth date with just the birth year or an age range, a specific ZIP code with a larger geographic area, or a specific occupation with a broader job category. This is a core technique used to achieve k-anonymity. While it reduces […]

Chile’s New Data Protection Law: Context, Overview, and Key Takeaways
[…] meet international commitments1. According to the Chilean government, the approved LPPD pursues the dual objective of (i) providing stronger protection for data subjects and (ii) regulating and promoting the country’s digital economy.2 This blog covers some of the new features in the LPPD, including: Extraterritoriality: the new law applies to private and public organizations […]

Minding Mindful Machines: AI Agents and Data Protection Considerations
[…] design features and characteristics may make them susceptible to new kinds of security threats. Adversarial attacks on LLMs, such as the use of prompt injection attacks to get these models to reveal sensitive information (e.g., credit card information), can impact AI agents too. Besides causing an agent to reveal sensitive information without permission, prompt […]