Social media is becoming
expensive to operate.
https://thehackernews.com/2026/08/tiktok-agrees-to-400-million-settlement.html
TikTok
Agrees to $400 Million Settlement in U.S. Child Privacy Lawsuit
The U.S.
Department of Justice (DoJ) announced on Friday that ByteDance-owned TikTok will
pay $400 million to settle a 2024 lawsuit accusing the company of
violating child privacy laws in the country.
… A
complaint filed
back in August 2024 alongside the Federal
Trade Commission (FTC) accused the company of "massive-scale
invasions of children's privacy" by knowingly allowing children
under 13 to create TikTok accounts and unlawfully collecting data
from those who used it in "Kids Mode."
A new product
test or something more sinister?
https://thenextweb.com/news/ox-alpha-stealth-model-openrouter-anonymous-provider
A
free AI model is winning over developers. And nobody knows whose
servers it runs on
An anonymous
model called Ox Alpha appeared on OpenRouter last week, free to use
with a million-token context window, and developers have been
impressed. OpenRouter’s own listing says prompts
and completions are retained by the unidentified provider.
A
model that nobody will take credit for is
being tested across the industry. Ox Alpha appeared
on OpenRouter last Thursday as a stealth release from an anonymous
third-party provider, free to use, with a context window of just over
a million tokens.
The
scale on offer is not modest. The open-source agent OpenCode said
the model would be free for a week with near unlimited usage, and
that its provider had capacity for 100 trillion tokens a day.
SciFi explains
everything?
https://scholarship.shu.edu/law-on-film-s04/14/
Episode
61: Blade Runner & Blade Runner 2049 (Guest: Frank Pasquale)
“The fault,
dear Brutus, is not in our stars, but in ourselves, that we are
underlings.”
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7305740
Artificial
Intelligence, Autonomous Systems and the Future of Legal
Responsibility
Fault-based
and even strict-liability doctrines share an
assumption that is quietly failing in the age of
autonomous systems: that
harm can be traced to a discrete, identifiable decision.
Reinforcement-learned
and continuously updated systems now generate outcomes that no
designer selected, no operator approved, and no single dataset
predicted. This article examines the resulting
"emergent-behavior gap"-the doctrinal space in which an
autonomous system's harmful output is neither a manufacturing defect,
a negligent act, nor a foreseeable design consequence, but the
product of adaptive processes that exceed the causal categories
liability law was built to trace. Using comparative doctrinal
analysis across the European Union, the United States, the United
Kingdom, Japan, South Korea, and Brazil, and engaging jurisprudence
including OQ v Land Hessen (C-634/21), Mobley v. Workday, and Uber
B.V. v. Aslam, the article argues that existing regimes-the EU's
revised Product Liability Directive, U.S. state tort law, the UK
Automated Vehicles Act 2024, and comparable Asian and South American
frameworks-remain adequate for foreseeable-risk harms but
structurally incapable of
allocating responsibility for genuinely emergent ones. It
proposes the Emergent Autonomy Liability Standard (EALS): a tiered
architecture combining certification-anchored strict liability for
classified risk profiles, mandatory behavioral logging ("autonomy
black boxes") to preserve causal traceability, and a no-fault
compensation fund, financed by risk-tiered contributions, for harms
that resist reconstruction under either fault or strict-liability
analysis. The article's original contribution is to reframe the
AI-liability debate away from who controlled the system toward
whether the harm is causally reconstructible at all, and to show why
that distinction, not the degree of automation itself, should
determine which liability instrument applies.
e-Moses says…
https://philpapers.org/rec/MEDTTC-2
The
Ten Commandments of Computer Ethics in the Age of Artificial
Intelligence
This chapter
revisits Ramon C. Barquin’s seminal “Ten Commandments of Computer
Ethics” in light of the transformative advances brought by
artificial intelligence. While Barquin’s 1992 framework provided
an accessible moral compass for computer professionals and users,
today’s AI-driven technologies demand a fresh ethical
interpretation. The chapter analyzes each commandment in the context
of contemporary challenges, including algorithmic bias, generative
AI, surveillance, misinformation, and autonomous decision-making. It
highlights the enduring value of Barquin’s principles—such as
integrity, fairness, and respect for privacy—while reinterpreting
them to address novel issues of accountability, explainability, and
human-AI collaboration. By bridging historical ethical guidance with
present-day debates, the chapter demonstrates how the commandments
can inform responsible AI governance and education. Ultimately, it
argues that updating, contextualizing, and teaching these principles
is essential for cultivating an ethical culture in the AI era,
ensuring that technology serves humanity with justice, dignity, and
transparency.