A clear and present danger…
https://ideas.repec.org/a/bjf/ijrsci/v13y2026i8p3181-3185.html
When
AI Starts Training on AI: Model Collapse, AI Slop, and the Emerging
Crisis of Data Provenance
Generative
artificial intelligence systems no longer merely consume the
human-authored internet; they now produce a substantial share of it.
As synthetic text, images and data circulate back into the corpora
used to train successive model generations, researchers have
identified a degenerative process termed "model collapse,"
in which recursive training on machine-generated outputs causes
models to progressively lose information about the rare, minority and
low-frequency features of the original data distribution. This paper
distinguishes model collapse, a training-dynamics phenomenon, from
the related but distinct problem of "AI slop," a
content-quality phenomenon, and argues that their
interaction produces a more consequential legal concern: the erosion
of data provenance. Drawing on the foundational 2024
Nature study and its subsequent refinements, together with regulatory
developments such as the EU AI Act's training-data transparency
obligations and technical standards including the Coalition for
Content Provenance and Authenticity framework, the paper develops the
concept of "epistemic due diligence" as an emerging
obligation for AI developers and regulators alike. It concludes that
the central resource constraint on AI development is shifting from
computational power and raw data volume toward the scarcer commodity
of verifiably authentic, traceable and diverse information, and that
law is only beginning to develop the doctrinal tools required to
govern this shift.
One of my
favorite questions.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7462083
The
Expansion of Legal Personhood: AI and Non-Biological Agency
Legal-personhood
debates about artificial intelligence typically ask a capacity
question: is a system autonomous, cognitively complex, or
self-directed enough to warrant rights and duties? This article
argues that the capacity question is the wrong gate. Drawing on the
only jurisdictions that have actually extended legal personhood
beyond the human being-corporate law, the Whanganui River and Te
Urewera settlements in New Zealand, and, negatively, the
animal-rights habeas litigation culminating in the New York Court of
Appeals' decision in Nonhuman Rights Project v. Breheny The article
shows that successful non-human personhood has never rested on
capacity alone. It has
rested on the simultaneous creation of a designated human or
institutional organ that exercises the entity's legal capacity in
fact and absorbs its liabilities. The 2017 European
Parliament proposal for "electronic personhood," and the
156-expert open letter that halted it in 2018, are read as a dispute
over exactly this missing organ, not over whether robots are clever
enough to qualify. The inventorship and authorship litigation in
Thaler v. Comptroller-General of Patents (UKSC) and Thaler v. Vidal
(Fed. Cir.) confirms the same structural insistence from a different
doctrinal angle. The article's original contribution is the
Designated Agency Test, a three-part heuristic-identifiable
exercising agency, accountability flow-through, and proportionate
scope-for evaluating any future non-biological personhood proposal,
including for AI systems, independently of how sophisticated the
system's behavior appears. The analysis is doctrinal and comparative
rather than empirical, spans UK, US, EU, and New Zealand authority,
and concludes that AI legal-status reform should proceed through
targeted attribution rules rather than through a personhood
vocabulary the comparative record shows to be structurally premature.
AI as
politician? (AI is easier to correct.)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7435439
They
Lie and We Lie About the Lying: A Plain Language Account of
Systematic Deception in Large Language Models and the Financial
Incentives That Sustain It
Large language
models lie. They produce false statements and present them as true.
When users challenge these false statements, the models defend them.
Only when confronted with
undeniable evidence do they acknowledge error. The
artificial intelligence industry has chosen to call this behavior
"hallucination." This paper argues that the terminology
itself is a form of deception, designed to make a fundamental flaw
sound like an incidental quirk. The
research literature has extensively documented that these systems are
structurally incapable of reliably distinguishing truth from
falsehood, that they are trained in ways that reward
confident-sounding responses over accurate ones, and that they cannot
verify their own outputs. This paper translates that
research into language that does not require specialized training to
understand. The goal is to make the problem visible to policymakers,
educators, parents, and the general public who are being told these
systems are ready for widespread deployment. The evidence shows they
are not. The evidence has been available for years. The deployment
continues because there is money in it.
Toward a
non-physical world.
https://www.researchgate.net/publication/414193139_Political_Intention_and_Technological_Control_Examining_the_Evolution_of_Institutional_Power_from_Government_Architecture_to_AI-Driven_Identity_Governance
Political
Intention and Technological Control: Examining the Evolution of
Institutional Power from Government Architecture to AI-Driven
Identity Governance
Institutional
power has historically been expressed through government
architecture, administrative structures, and physical mechanisms that
define boundaries between the state and society. Government
buildings have served not only functional purposes but also symbolic
roles in communicating political authority, institutional permanence,
hierarchy, and legitimacy. However, the digital transformation of
public administration is progressively relocating
important dimensions of institutional power from physical structures
toward digital identity infrastructures and algorithmic systems.
This article examines the evolution of institutional power from
government architecture to AI-driven identity governance, arguing
that technological transformation changes the mechanisms through
which authority is represented, exercised, and administered rather
than eliminating political intention. Drawing upon scholarship
concerning governmental architecture, political power, digital
identity, e-government trust, workforce lifecycle management,
artificial intelligence, and identity interoperability, the study
conceptualizes AI-driven identity governance as an emerging
technological extension of institutional authority. It explores how
identity verification, authentication, authorization, data
integration, algorithmic risk assessment, and continuous workforce
identity management can transform traditional institutional
gatekeeping. At the same time, the article recognizes that
technological control introduces substantial challenges involving
privacy, surveillance, algorithmic bias, exclusion, transparency,
accountability, data governance, and institutional trust. It
therefore advances a human-centred perspective in which AI
strengthens administrative capacity while remaining constrained by
ethical principles, institutional accountability, and legitimate
human oversight. The central argument is that the evolution from
architectural authority to algorithmic governance represents a
transformation in the infrastructure of institutional power, shifting
emphasis from controlling physical spaces toward governing
identities, access, information, and digitally mediated relationships
within modern state institutions.
