If it exists, it’s taxable.
https://pogowasright.org/the-irs-says-your-digital-life-is-not-your-property/
The IRS Says Your Digital Life Is Not Your Property
Brent Skorup and Laura Bondank write:
When the IRS secretly demands your financial records and private information from a third party, without a warrant, what rights do you still have?
That’s the question at the heart of Harper v. O’Donnell, which is before the Supreme Court. New Hampshire resident Jim Harper is fighting back against the IRS after discovering he was swept up in a massive digital dragnet. The case could redefine how the Fourth Amendment applies in the age of cloud storage—and it may determine whether your emails, location history, search queries, and financial records that tech companies store on your behalf are treated as your property.
In 2016, the IRS ordered the cryptocurrency exchange Coinbase to hand over transaction records of over 14,000 customers. Harper was among them and only learned of the government’s records grab after the IRS sent him a warning letter, mistakenly suggesting he’d underreported his cryptocurrency income. He soon discovered the IRS had his transaction logs, wallet addresses, and public keys—allowing the agency to monitor any future transactions he made.
Harper hadn’t done anything wrong. He’d simply used a legal platform to buy and sell cryptocurrency. But his digital footprint became visible to the government overnight.
Read more at Reason.
Sorry, the AI says we shouldn’t waste time treating you.
Predictive AI Models for Emergency Room Triage
Emergency room (ER) triage is a critical process that prioritizes patients based on the severity of their conditions, aiming to ensure timely care in high-pressure environments. However, traditional triage methods are often subjective and may lead to delays in treatment, overcrowding, and suboptimal patient outcomes. This paper explores the role of predictive Artificial Intelligence (AI) models in enhancing ER triage by providing data-driven, real-time insights to optimize decision-making, improve patient prioritization, and streamline resource allocation. We examine various AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), highlighting their application in analyzing structured and unstructured data such as electronic health records (EHRs), patient vital signs, medical imaging, and clinical notes. The paper also discusses the importance of data preprocessing, including handling missing values, data normalization, and feature selection, to ensure accurate model predictions. Through case studies and clinical implementations, we demonstrate how AI models have been successfully integrated into real-world ER settings to predict patient acuity, early deterioration, and patient outcomes. Ethical, legal, and practical considerations such as data privacy, algorithmic bias, and model transparency are also addressed. The paper concludes with a discussion on the future directions of AI in ER triage, including the integration of multimodal data, real-time monitoring, and personalized care. Predictive AI has the potential to significantly enhance ER efficiency and improve patient care, making it a valuable tool for modern healthcare systems.
AI is no big deal?
https://scholarship.law.unc.edu/cgi/viewcontent.cgi?article=1508&context=ncjolt
Liability for AI Agents
Artificial intelligence (“AI”) is becoming integral to modern life, fueling innovation while presenting complex legal challenges. Unlike traditional software, AI operates with a degree of autonomy, producing outcomes that its developers or deployers cannot fully anticipate. Advances in underlying technology have further enhanced this autonomy, giving rise to AI agents: systems capable of interacting with their environment independently, often with minimal or no human oversight. As AI decision-making—like that of humans—is inherently imperfect, its increasing deployment inevitably results in instances of harm, prompting the critical question of whether developers and deployers should be held liable as a matter of tort law.
This question is frequently answered in the negative. Many scholars, adopting a framework of technological exceptionalism, assume AI to be uniquely disruptive. Citing the lack of transparency and unpredictability of AI models, they contend that AI challenges conventional notions of causality, rendering existing liability regimes inadequate.
This Article offers the first comprehensive normative analysis of the liability challenges posed by AI agents through a law-and-economics lens. It begins by outlining an optimal AI liability framework designed to maximize economic and societal benefits. Contrary to prevailing assumptions about AI’s disruptiveness, this analysis reveals that AI largely aligns with traditional products. While AI presents some distinct challenges—particularly in its complexity, opacity, and potential for benefit externalization—these factors call for targeted refinements to existing legal frameworks rather than an entirely new paradigm.
This holistic approach underscores the resilience of traditional legal principles in tort law. While AI undoubtedly introduces novel complexities, history shows that tort law has effectively navigated similar challenges before.
For example, AI’s causality issues closely resemble those in medical malpractice cases, where the impact of treatment on patient recovery can be uncertain. The legal system has already addressed these issues, providing a clear precedent for extending similar solutions to AI. Likewise, while the traditional distinction between design and manufacturing defects does not map neatly onto AI, there is a compelling case for classifying inadequate AI training data as a manufacturing defect—aligning AI liability with established legal doctrine.
Taken together, this Article argues that AI agents do not necessitate a fundamental overhaul of tort law but rather call for targeted, nuanced refinements. This analysis offers essential guidance on how to effectively apply existing legal standards to this evolving technology.
Who really done it?
https://ijlr.iledu.in/wp-content/uploads/2025/04/V5I723.pdf
ARTIFICIAL INTELLIGENCE, LEGAL PERSONHOOD, AND DETERMINATION OF CRIMINAL LIABILITY
The broad adoption of artificial intelligence (AI) across vital domains ranging from autonomous vehicles and financial markets to healthcare diagnostics and legal analytics has exposed significant gaps in our legal systems when AI-driven errors or malfunctions cause harm. Autonomous systems often involve multiple stakeholder hardware suppliers, software developers, sensor manufacturers, and corporate overseers making it difficult to pinpoint who is responsible for a system’s failure. The 2018 Uber autonomous-vehicle crash in Tempe, Arizona, where a pedestrian was misclassified repeatedly by the AI’s perception module and the emergency braking function was disabled, underscores this challenge: with safety overrides turned off and state oversight minimal, liability became entangled among engineers, operators, and corporate policy not the machine alone.
Traditional criminal law doctrines rest on actus reus (the guilty act) and mens rea (the guilty mind), both premised on human agency and intent. AI entities, however, can execute complex decision-making without consciousness or moral awareness, creating a “responsibility gap” under current frameworks. To bridge this gap, scholars like Gabriel Hallevy have proposed three liability models—perpetration-via-another (holding programmers or users accountable), the natural-probable-consequence model (liability for foreseeable harms), and direct liability (attributing responsibility to AI itself if it meets legal thresholds for actus reus and an analogue of mens rea). Each model offers insight but struggles with AI’s semi-autonomous nature and opacity.
This paper argues against prematurely conferring legal personhood on AI an approach that risks absolving human actors and diluting accountability. Instead, it advocates for a human-centric policy framework that combines clear oversight duties, mandated explainability measures, and calibrated negligence or strict-liability standards for high-risk AI applications. Such reforms are especially urgent in jurisdictions like India, where AI governance remains nascent. By anchoring liability in human oversight and regulatory clarity rather than on machines themselves, we can ensure that accountability evolves in step with AI’s growing capabilities, safeguarding both innovation and public safety.
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