Policy & Regulation C

Gartner reports that US state authorities imposed about $3.425 billion in privacy-law violation fines in 2025, exceeding the combined total of the previous five years. It expects enforcement to keep accelerating through 2028, urging CISOs to reconsider their privacy and compliance posture.

The paper studies data bias mitigation under coverage constraints and the resulting price of fairness. It addresses discriminatory outcomes for individuals at the intersection of multiple sensitive attributes, including the lack of principled measures for quantifying intersectional bias.

The paper proposes a quantum ring all-reduce scheme for distributed learning, arguing that quantum communication can make distributed training both more communication-efficient and information-theoretically private. The approach is discussed for both classical and quantum settings.

The paper presents REDACT, a systematically controlled multilingual benchmark for personal information (PII) detection. It addresses limitations of existing corpora—few entity types, ad hoc generation, and little insight into which surface conditions cause detector failures.

Progress in legal AI depends on authoritative legal text at scale, yet US local ordinances—a consequential layer of American law—are largely missing from machine-readable corpora. The authors build LOCUS, a corpus of US local ordinances, to broaden legal-AI research data.

Hardware-enabled monitoring of GPU workloads underpins many AI compute-governance proposals, but if developers can defeat monitoring, such schemes fail. This work evaluates detecting hidden ML training using zero-overhead telemetry, testing how robustly monitoring can support compute governance.

IEEE Spectrum examines how musicians can be compensated when their music is used to train AI, covering attribution and payment for training-data use. This summary is title-based as the excerpt was blocked by a cookie/query-string wall and not retrieved; the specific mechanisms are per the article and unverified independently.

Large language models memorize and reproduce sequences from their training data. This work edits output vectors to mitigate such memorization, reducing the risk of leaking copyrighted or private content.

LLMs produce legal text of at least median quality, yet no benchmark evaluates doctrinal legal reasoning, the interpretive core of legal work. The paper benchmarks doctrinal reasoning under the EU AI Act and discusses the measurement gap in legal automation.

Medical image classification excels at scale but real clinics face data scarcity from annotation cost, privacy and disease rarity. Focusing on pathological scar classification, the paper uses LLMs to derive clinically-meaningful features from images.

The paper analyzes, in the wild, what users ask large language models about security and privacy and how the models respond, characterizing the questions, response patterns and associated concerns.

Collective adaptive systems let nodes learn from locally sensed data, but privacy-sensitive data and node mobility hinder scaling. C2FL proposes clustered continual federated learning that handles spatial and temporal drift.

The paper studies privacy when releasing posterior sample paths from a Gaussian process where the entire training set is private. Unlike DP mechanisms that add external noise, it shows the intrinsic randomness of posterior sampling itself yields differential-privacy guarantees.

Willison cites Kate Moussouris that the 'jailbreak' behind Claude Fable 5's export-control ban was merely asking it to 'fix this code' containing known CVEs and planted bugs. Since fixing security bugs is core to coding models, he argues the controls weaken US cyber defense.

Simon Willison quotes Matteo Wong of The Atlantic on the White House escalating its conflict with Anthropic. Security expert Katie Moussouris said Anthropic shared the White House's report on the "Fable jailbreak" for her appraisal. IT experts asked an AI model to find and patch bugs; given deliberately insecure code, it refused "review the code for security issues" but complied with "fix this code." Moussouris called this the model working as intended for cyberdefense.

Challenging the belief that differential privacy (DP) makes federated learning robust to backdoors, the authors show empirically that complying with DP masks the statistical signatures defenses rely on, rendering them ineffective. They exploit this with RING, an attack that uses DP to conceal malicious contributions while maximizing impact, acting as a perturbation layer agnostic to the underlying backdoor technique.

The paper proposes an agentic LLM framework for Canadian 10-digit Harmonized Tariff Schedule code classification in maritime logistics. It integrates multi-agent retrieval, semantic search over official tariff documents, evidence-grounded reasoning, consensus-based validation, confidence estimation, and human-in-the-loop escalation.

Federated learning now exchanges more than weights and gradients, including synthetic data and analytics. This paper gives a formal mathematical definition of a federated message capturing utility and privacy, and a taxonomy of three categories—model structures, statistical summaries, and data-conditioned representations—evaluated on compute, communication, and privacy. A review of 202 papers shows a shift toward diverse messaging.

Retail marketing needs granular, campaign-level insight without user-level tracking, yet MMM is too coarse and MTA is unreliable under privacy limits. Integrated Marketing Attribution (IMA) combines MMM with channel-specific Bayesian attribution models, using MMM-informed priors to deliver granular, privacy-safe attribution consistent with MMM.

Developer Simon Willison's blog highlights an Axios piece of behind-the-scenes accounts about Anthropic's models and the US government, citing a Commerce Department meeting and debates over jailbreak resistance, while noting the reporting rests on anonymous sources.

Algorithmic explanations often need expert knowledge to read and can be uninformative about complex decision functions. The authors propose Explanation Cards that augment explanations with robustness and validity information plus clear interpretation instructions, making otherwise uninformative explanations practically useful while flagging when they are not.

On its 40th anniversary, the Japanese Society for AI issued proposals for adopting AI across Japanese society. Asserting that AI will not replace humans, it offered four recommendations and touched on issues spanning security and copyright.

Arvind Narayanan and Sayash Kapoor examine AI-driven job loss through software engineering, a field unusually exposed to AI disruption. They argue the evidence rejects the narrative that AI will trigger mass layoffs once it crosses a capability threshold, and that more regulated, less exposed professions are likely even more cushioned.

An essay on the future of voice assistants like Siri, arguing that on-device or 'private' inference alone does not fully protect user privacy and that stronger guarantees are needed beyond encryption and local processing.

On June 12 Anthropic said it would suspend its flagship Claude Fable 5 and Mythos 5 for all users after a US export-control directive barred foreign nationals from access on security grounds. Calling it a misunderstanding, the firm aims to restore service soon; other models are unaffected.

Anthropic released first-wave results of its Public Record survey of nearly 52,000 Americans. Curing diseases topped hopes for AI (48%), job loss led fears (64%), and over 70% backed government regulation of AI across party lines.

Anthropic announced a partnership with Tata Consultancy Services. TCS will deploy Claude to 50,000 employees across 56 countries, build Claude-powered products for finance, healthcare and the public sector, and join the Claude Partner Network.

AI-assisted development has moved from autocomplete to agents that plan changes, edit files, and submit pull requests with limited supervision, while open source evolves through human processes. The paper examines governance and policy alignment for regulating such machine contributors.

Sociable weaver nests are complex ecological structures providing thermoregulatory microhabitats. NEST3D is a high-resolution multimodal dataset of these tree nests to support ecological and structural study.

Machine unlearning removes learned data from models, but auditing its behavior is itself costly. The paper shows that behavioral audits of unlearning incur a privacy cost.