This interim final rule (IFR) amends U.S. Department of Homeland Security (DHS) regulations to provide: USCIS may require mandatory electronic filing (e-filing) of certain benefit requests; the process USCIS will follow to require a benefit request to be e-filed; and how a waiver of the e-filing requirement for those individuals unable to file electronically may be requested. This rule is intended to increase digital intake and processing to move USCIS and requestors from a mostly paper process to an electronic process and further enhance the integrity of the immigration system and the security of the United States.
This week on Uncanny Valley, we dig into the latest prediction market buzz, Flock’s AI-powered police search tool, and how tech bros don’t know how to talk about “rouge” AI agents
WIRED rebuilt Flock’s latest search tool from code the company sends to a police officer’s browser. Its AI can keep watch across multiple cameras for anyone fitting a written description.
Not long ago, I rented an SUV from a well-known car rental company. Within hours of an employee scanning my driver's license, a high-resolution scan of my ID was available for sale on the dark web.
An exposé published Tuesday by KrebsOnSecurity reports that my license was one of more than 153 million that were available through Nexus, the name of the new ID theft service. Like other driver's licenses available there—including some belonging to journalist Brian Krebs, his mother, an FBI assistant director, and several security researchers—my license was purported to include multiple image files showing both the front and back of the ID. Besides a basic image scan, the files also captured the images in the infrared and ultraviolet spectrums. Presumably, the additional formats may allow cloned-based counterfeit IDs to pass hologram tests.
Growing by the day
Besides advertising the availability of driver's licenses, Nexus offered to sell a bevy of other forms of ID. They included:
In this document, the Federal Communications Commission ("FCC" or "Commission") adopted a Further Notice of Proposed Rulemaking that seeks comment on proposed rules intended to make the Emergency Alert System (EAS) and Wireless Emergency Alerts (WEA) more resilient, flexible, and useful.
After an affair with a fellow police officer ended, a Georgia cop used Flock to track her movements—and those of a man whose vehicle often showed up near hers, internal investigation records show.
The Department of Justice ("the Department") is implementing criteria to guide determinations for granting relief from disabilities imposed by federal laws with respect to the acquisition, receipt, transfer, shipment, transportation, or possession of firearms. The criteria are designed to ensure that the fundamental right of the people to keep and bear arms is not unduly infringed, that those people granted relief are not likely to act in a manner dangerous to public safety, and that granting such relief would not be contrary to the public interest.
Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.
“Free people search” is the most abused phrase on the internet. The standard experience: you type a name, watch a fake progress bar “scan millions of records” for three minutes, and land on a checkout page. The search was free. The answer costs $28.05 a month. This list applies a stricter definition. Every site below […]
This final rule will revise the Medicare hospital inpatient prospective payment systems (IPPS) for operating and capital-related costs of acute care hospitals; make changes relating to Medicare graduate medical education (GME) for teaching hospitals; update the payment policies and the annual payment rates for the Medicare prospective payment system (PPS) for inpatient hospital services provided by long-term care hospitals (LTCHs); update and make changes to requirements for certain quality programs; and make other policy- related changes. ONC also adopts certain health information technology (health IT) standards and specifications on behalf of HHS.
The Architectural and Transportation Barriers Compliance Board (Access Board or Board) is issuing this Notice of Proposed Rulemaking (NPRM) to update its regulations under the Freedom of Information Act (FOIA). The Board proposes to replace its existing FOIA regulations with this proposed rule, which streamlines the language of several procedural provisions; updates procedures consistent with current technology; incorporates changes required by amendments to the FOIA under the OPEN Government Act of 2007 and the FOIA Improvement Act of 2016, and developments in case law; and conforms to Department of Justice guidelines for agency FOIA regulations.
You likely check the locks on your doors before heading to bed every night. You keep your physical valuables tucked away from prying eyes, yet the most sensitive details of your life now live entirely in the cloud. We often treat our devices like permanent storage lockers, leaving front doors wide open across the internet. […]
Researchers tested top image editing models on Hugging Face and found they could easily create explicit deepfakes—and 1,000 image editing prompts show how people use the software.
The City Attorney’s Office sent the tech giants cease-and-desist letters this week telling them to stop profiting from 13 “face-swap” apps that are overwhelmingly used to target women and girls.
This proposed rule addresses: changes to the physician fee schedule (PFS); other changes to Medicare Part B payment policies to ensure that payment systems are updated to reflect changes in medical practice, relative value of services, and changes in the statute; codification of establishment of new policies for: the Medicare Prescription Drug Inflation Rebate Program under the Inflation Reduction Act of 2022; the Ambulatory Specialty Model; updates to drugs and biological products paid under Part B; Medicare Shared Savings Program requirements; updates to the Quality Payment Program; updates to policies for Rural Health Clinics and Federally Qualified Health Centers; update to the Ambulance Fee Schedule regulations; codification of the Inflation Reduction Act and Consolidated Appropriations Act, 2026 provisions; updates to Clinical Laboratory Fee Schedule regulations; updates to the Medicare Promoting Interoperability Program.
The Federal Deposit Insurance Corporation (FDIC) is inviting comment on a notice of proposed rulemaking that would update, clarify, and supplement the FDIC's regulations regarding the disclosure of confidential information by the FDIC and other parties, including by enhancing the ability of insured depository institutions to share confidential supervisory information with affiliates and certain other entities for appropriate business purposes, without seeking prior authorization from the FDIC. The proposal also would significantly simplify and clarify the requirements and restrictions applicable to the FDIC's discretionary disclosure of confidential information. Finally, the proposal would update and simplify the FDIC's rules regarding disclosures required under the Freedom of Information Act and would clarify how and when FDIC information may be disclosed in connection with legal proceedings and as a result of service of process made upon the FDIC and its directors, officers, and employees.
The U.S. Nuclear Regulatory Commission (NRC) is proposing to revise its regulations to modernize security and fitness-for-duty requirements to enhance efficiency, consistent with Executive Order 14300, "Ordering the Reform of the Nuclear Regulatory Commission." The proposed revisions are intended to reduce regulatory burden, where appropriate, while continuing to provide reasonable assurance that safety and security will be adequately maintained at NRC-licensed facilities.
As our daily activities become more digitised, from ordering dinner to receiving medical care, the amount of data produced by humans and machines continues to grow each year. The internet provides a seemingly limitless flow of accessible data of all formats and natures, from news sites to social media. In early 2026, the internet archive initiative CommonCrawl boasted over 300 billion webpages in its database. Adding to this digital abundance, even larger volumes of data remain underused and locked in organisations’ private databases. Meanwhile, IBM reports that in 2024, the biggest challenge for AI developers was a shortage of high-quality data. This is the AI data paradox: despite an abundance of data globally, AI developers face a scarcity of usable data, with growing expert concerns about a looming data crunch as reported by the OECD (2025).
Scraped internet data as a key source for AI training
Data is the cornerstone of modern AI development, from model training to grounding. As online public content continues to grow, it has become a major source of training data for AI models. Initiatives such as CommonCrawl and LAION have harvested, or “scraped”, billions of online pages and images, ranging from news articles and government websites to blogs and social media. These datasets fuel rapid advances in AI tools, but also pose deep challenges.
Figure 1: The AI data value chain: From scraping data for reuse to content to generation
Contemporary AI requires contemporary data sourcing methods
As investment and revenue flow, AI has become a major industry, but data-sourcing methods have not kept pace. Many current AI processes still rely heavily on scraping large amounts of public content, often without permission, proper compensation or quality controls. But this “grab what you can” approach has limits. As the report documents, over 50 copyright and data protection lawsuits have been filed worldwide, while websites are increasingly deploying technical and contractual barriers to prevent scraping. At the same time, the web is increasingly saturated with low-quality material, including AI-generated “slop” and disinformation. Meanwhile, vast amounts of valuable data remain locked away on private servers because concerns about legal risk and confidentiality inhibit sharing.
These quality challenges and legal pressures point to the need for a new approach, one that moves beyond extraction towards mutually beneficial data-sharing arrangements, including commercial and non-commercial agreements as described in the OECD’s Mapping Relevant Data Collection Mechanisms for AI Training report (2025). Instead of relying on a single technical solution, the VIADUCT report presents data sourcing as a systemic challenge at the intersection of law, economics and technology. While data infrastructures have expanded significantly, experience shows that technical capacity alone does not initiate data flows.
Data sharing as a transaction
If not a simple engineering problem, then what is data sharing? At its most basic, data sharing is a transaction between two parties.
For common assets such as treasury bonds, Brent crude oil or soda cans, industry standards, regulations and institutions support transactions and build market trust. By contrast, data is not a standard asset. It is a collection of diverse assets that take different forms, with varying stakeholders and regulatory requirements. Consider two datasets: one with hospital patients’ X-rays, and another with archived news articles. The first dataset contains sensitive personal information protected under the GDPR in Europe and must comply with the principles of confidentiality and consent. News articles in the second dataset are copyrighted works whose owners can prohibit reproduction and opt out of AI training under EU law. These simple examples show that sharing data cannot rely on technology alone but must also consider a dataset’s economic, legal and social contexts and constraints.
Data is not monolithic
Government officials and business leaders often use metaphors like “raw material”, “new oil” and “gold” to describe data. However, contrary to what these metaphors suggest, data is not a uniform resource. In its EASD in the age of AI (2025), the OECD describes data as “recorded information in structured or unstructured formats, including texts, images, sound, and video”, which may also include AI models themselves when training data is memorised. On top of its diversity of shapes and formats, data is also a mosaic of digital assets, each governed by different rules, logic and constraints. Is one dealing with a copyrighted news article? Personal health records? A company’s trade secrets? Government statistics? Open source software code? Understanding this is essential to designing sustainable AI data ecosystems. Each type of data has its own legal guardrails, economic dynamics and technical challenges.
The VIADUCT report classifies data into five governance regimes according to the EU legal framework:
Figure 2: Data governance regimes under EU law
Governance model
Data scope
Dataset example
Copyrighted content
All creative works, including texts, images, videos, sounds, software source code and certain databases. Authors have exclusive rights to reproduce, communicate and distribute their works. They can also opt-out of commercial AI processing.
News article dataset, book archive, social media posts, music database
Personal data
Any data or information relating to an identified or identifiable individual (“data subject”). Under EU’s GDPR, a data processing, such as AI training, must be transparent, confidential, minimal and motivated by legal grounds such as data subject consent, or controller’s legitimate interest.
Customer history database, patients’ medical records, mobile phone GPS locations
Trade secrets
A dataset which is secret, is safeguarded and holds value due to its secrecy. Companies are protected against unlawful access, and can share trade secrets with third parties under strict safeguarding measures.
Engineering blueprint files, pharmaceutical molecule database, commercial lead database
Public sector data
Under EU law, public sector bodies must publish their documents for commercial and non-commercial reuse, free from exclusive license and without fee beyond cost compensation.
Texts of law, national statistics, national company registry
Open data
Any data which can be accessed, shared and used by anyone for any purpose, free of charge. In Europe, this may include contents with expired copyrights, government documents, and open-license content
19th century books, Wikipedia articles, open-source software code
Three guiding principles for sustainable and responsible approaches to data sharing for AI
In this diverse and complex environment, the report sets out three clear guiding principles that apply to all data types and governance regimes to foster sustainable and ethical data sharing for AI.
Legal compliance is the first principle. Data sharing and downstream use must respect applicable legal frameworks. In Europe, this includes honouring copyright holders’ rights, establishing legal grounds for the use of personal data and protecting confidential trade secrets.
The second is trust. Data holders need assurance that their data will be used only for permitted or legitimate purposes and protected by robust cybersecurity measures. Data consumers, in turn, need confidence that datasets are legitimate, accurate and of high quality.
The third principle is fairness. Data-sharing arrangements should be mutually beneficial, crediting organisations that support AI development and, where appropriate, sharing the benefits.
Figure 3: Ethical data sharing principles and best practices
Constraints for ethical data sharing
These principles sound simple, but applying them in practice is challenging. Exchanging large volumes of data requires expertise, which is often lacking in smaller organisations, and the process is fraught with technical, legal, and economic frictions. Data quality issues are a major source of friction, as errors, inappropriate content or bias can create risks and costs for downstream AI use. Ensuring data holders maintain control of their data after sharing or publishing is essential. This is referred to as digital self-determination. This is the case for personal data, copyrighted content or trade secrets, for which the data holder must often grant permission for each new processing act. EU law imposes strict confidentiality standards for personal data, trade secrets and sensitive government data. Finally, to incentivise data sharing and ensure its sustainability for data holders, implementing appropriate economic models such as cost and value compensation appears to be an essential solution.
Figure 4: Ethical data sharing constraints
Transitioning to a multidisciplinary and implementation-focused approach
This non-exhaustive list of challenges illustrates the limits of relying only on normative or technical responses to address data-sharing constraints. The report investigates alternative approaches, such as opt-out methods, smart contracts, data attribution and privacy-enhancing technologies.
When it comes to ethical data sharing, moving from Is it possible? to How can we implement it? requires developing actionable tools and recommendations to address challenges. Given their diverse and context-sensitive nature, VIADUCT facilitates dialogue among data holders, AI developers, policymakers and researchers on concrete data-sharing projects and experiments. Ultimately, this exercise seeks to create a better understanding of the challenges for data sharing and promote and test innovative solutions at the crossroads of technology, economics and law.
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