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.