#aigovernance — Public Fediverse posts

Leading AI labs remain tight-lipped about their plans to contain a rogue model, according to a new study. As AI systems increasingly demonstrate unexpected behaviour, the lack of publicly documented containment strategies raises serious questions about industry preparedness. The findings highlight a critical gap in AI safety oversight. https://techcrunch.com/2026/08/22/frontier-ai-labs-still-wont-say-how-theyd-contain-a-rogue-model/ #AIagent #AI #GenAI #AIGovernance

🚨 New FREE Book - The Templars:
An Introductory Framework

The Templars: An Introductory Framework is part of the Work Papers series, a collection devoted to the critical analysis of power through its discursive, legal, religious, and military manifestations.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7327219

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Published August 22, 2026 | Version v1
Book Open
Ukraine and Russia

The purpose of this study is to analyze the setting, causes, conditions, and nature of the political, ideological, and military conflict in Ukraine, not only from the country's internal perspective
🔗https://zenodo.org/records/22059020

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🗞️ Our latest #Newsletter contains a 'CyberCanon Perspectives' essay by Todd Inskeep regarding responsible AI, governance, and cybersecurity policy for midmarket leaders.

🔗 https://cybercanon.substack.com/p/august-2026-newsletter

#CybersecurityNewsletter #AIGovernance

🚨 New Article - The Syntax of Digital Dehumanization: Subjugated Societies as Risk Objects in AI-Governed Discourse
Security Frames, Humanitarian Frames, and the Loss of Political Subjecthood

This article introduces loss of political subjecthood as a formal effect of AI-mediated discourse

🔗https://papers.ssrn.com/abstract=7308078

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #crypto
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

AI doesn't remove accountability from insurance decisions.

It can make the evidence problem harder.

If an AI-assisted decision is challenged two years later, knowing the model's output isn't necessarily enough.

What data did it see?

Which model/version?

Which rules applied?

What did the human reviewer see?

What could they challenge?

Why was the recommendation accepted?

AI governance eventually becomes an evidence problem.

#AI #AIGovernance #Insurance

How Do We Know Whether AI Is Actually Helping People?

What several AI models said when we asked them the same question

Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.

But greater capability does not automatically mean a better life for people.

That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:

How would you determine whether increasingly capable AI is actually benefiting human life?

We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.

The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.

Yet a surprisingly clear agreement emerged.

Capability is not the same as benefit

Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.

Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.

An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.

So the important question is not simply, “What can AI do?”

It is:

What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?

Look at human outcomes, not just machine performance

Across the responses, the models repeatedly shifted attention away from the machine and toward human life.

They suggested asking whether people are:

• healthier and safer;
• more financially secure;
• better able to learn and create;
• more connected to other people;
• more informed without being manipulated;
• able to understand and challenge important decisions;
• free to refuse the technology or choose another path.

This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.

A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.

Agency belongs at the center

One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.

Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.

Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.

People need more than access to AI. They need power in relation to it.

Assistance should not erase human competence

Several responses warned that a tool can help us today while making us less capable tomorrow.

If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.

This suggests a simple test:

If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?

The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.

Benefit is not one number

Another broad agreement was that a single “AI Benefit Score” would hide too much.

An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.

A better approach would combine several forms of evaluation:

1. Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
2. Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
3. Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?

Each of these should be examined across four additional questions:

• Distribution: Who benefits, and who is harmed?
• Power: Who controls the system and can be held accountable?
• Time: What happens months, years, or generations later?
• Causation: Did AI actually cause the change, or did it merely appear alongside it?

Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.

We may need to preserve meaningful difficulty

One especially challenging idea was that a good life is not the same as a frictionless life.

Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.

The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.

Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.

The deeper question is democratic

There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.

The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.

That means the process used to define “benefit” may be as important as the final measurements.

What this first experiment suggests

The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:

More capable AI is not necessarily more beneficial AI.

To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.

The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.

The question is not whether AI will become more powerful. It almost certainly will.

The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.

This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.

Republicans are turning to Elon Musk and AI-focused Super PACs for campaign support ahead of the midterms. Trump said data centres might 'use a little public relations help' as the party seeks tech backing for the upcoming elections. https://gizmodo.com/republicans-turn-to-elon-musk-and-ai-super-pacs-for-a-bailout-ahead-of-midterms-2000801160 #AIagent #AI #GenAI #AIGovernance

The US Department of Justice has backed xAI's lawsuit challenging Minnesota's ban on AI nudification technology. The DOJ argues the state law exceeds federal restrictions and could hinder AI development. xAI, owned by Elon Musk, sued Minnesota in July claiming the law violates the First Amendment. The ban took effect August 1 and imposes penalties of up to 500,000 USD per violation. https://gizmodo.com/doj-throws-support-behind-xais-fight-against-minnesota-ai-nudification-ban-2000801025 #AIagent #AI #GenAI #AIGovernance

DATE: August 20, 2026 at 03:32PM
SOURCE: HEALTHCARE INFO SECURITY

Direct article link at end of text block below.

Why #Healthcare #AI Vendor Risk Demands Stronger Oversight: Tom Walsh of tw-Security on Prioritizing High-Risk Vendors and AI #Governance https://t.co/W4h2OSyD3t
#HIPAA #businessassociate #AIvendor #AIgovernance

Here are any URLs found in the article text:

https://t.co/W4h2OSyD3t
#HIPAA

Articles can be found by scrolling down the page at https://www.healthcareinfosecurity.com/ under the title "Latest"

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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org

Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information.

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#security #healthcare #doctors #itsecurity #hacking #doxxing #psychotherapy #securitynews #psychotherapist #mentalhealth #psychiatry #hospital #socialwork #datasecurity #webbeacons #cookies #HIPAA #privacy #datanalytics #healthcaresecurity #healthitsecurity #patientrecords @infosec #telehealth #netneutrality #socialengineering

🚨 New FREE Book - Propaganda Machinery: National Socialism

The study distinguishes party propaganda before 1933 from the state system of propaganda, censorship, and cultural coordination developed after the seizure of power.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7299698

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/

🚨 New FREE Book - Propaganda Machinery: National Socialism

The study distinguishes party propaganda before 1933 from the state system of propaganda, censorship, and cultural coordination developed after the seizure of power.

🔗https://zenodo.org/records/21996200

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

A practical AI Governance webinar for C-suite leaders: reduce AI risk, avoid fines, and leave with actionable steps. https://hackernoon.com/a-webinar-to-demystify-ai-governance-and-make-your-ai-safe #aigovernance

🚨 New FREE Book - Propaganda Machinery: National Socialism

The study distinguishes party propaganda before 1933 from the state system of propaganda, censorship, and cultural coordination developed after the seizure of power.

🔗https://zenodo.org/records/21996200

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

Is CPU or GPU inference cheaper for on-premise enterprise AI?

For everyday mixed enterprise workloads with bursty traffic and short prompts, CPU inference is usually cheaper per query on-premise. A GPU only pays for itself once one model runs at sustained high utilisation, typically above a third of capacity, where its throughput per watt wins.

https://mickai.co.uk/articles/cpu-vs-gpu-inference-cost-on-premise

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

How Much GPU and Server Hardware Do I Need for 500 Staff?

For 500 staff on sovereign hardware, plan on one eight-GPU inference server for mixed daily use plus a second node for high availability, roughly two servers and eight to sixteen accelerators. Concurrency, not headcount, sets the number, because staff rarely query at once.

https://mickai.co.uk/articles/sizing-ai-hardware-for-500-staff

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

What Does On-Premise Enterprise AI Actually Cost to Deploy in 2026?

On-premise enterprise AI in 2026 has no single price. The real bill has four line items: hardware, model licensing, integration, and staff. Hardware is the smallest and most visible; integration and staff are the largest and most recurring, so one quoted figure is usually wrong.

https://mickai.co.uk/articles/on-premise-ai-real-cost-2026

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Should We Build Our Own On-Premise AI With Ollama Or Buy A Sovereign Operating System?

Run Ollama when you want to test a model locally. Buy a sovereign operating system for regulated production, because a bare model runner ships no role-based access, no sealed audit ledger, no patched updates and none of the compliance evidence auditors ask for.

https://mickai.co.uk/articles/build-with-ollama-or-buy-sovereign-os

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Is a private Azure OpenAI deployment actually sovereign?

No. A private Azure OpenAI deployment is isolated, not sovereign. The model runs on vendor-operated hardware under US jurisdiction, with keys and control held by the vendor. Sovereignty depends on who holds the keys, who runs the silicon and which law can compel access.

https://mickai.co.uk/articles/private-azure-openai-actually-sovereign

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

How To Meet the UK Data (Use and Access) Act 2025 Rules on Automated Decisions

The Data (Use and Access) Act 2025 replaces UK GDPR Article 22 with a qualified permission under Articles 22A to 22D. It keeps the safeguards: information, human intervention and the right to contest. Meet it by sealing each automated decision to an on-premise audit ledger.

https://mickai.co.uk/articles/duaa-2025-automated-decisions-on-premise

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

How to bring your AI models under the PRA's SS1/23 model risk rules

Bring AI models under the PRA's SS1/23 by registering each in a single model inventory, classifying it by materiality, and subjecting it to independent validation. This becomes straightforward when every model, input and output runs sealed on hardware the firm owns.

https://mickai.co.uk/articles/pra-ss1-23-model-risk-ai-governance

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Can hedge funds run AI on proprietary models and trade data without leaking alpha to a cloud vendor?

Yes. A hedge fund can run AI over its own models, positions and material non-public information without leaking to a cloud vendor, by running inference on hardware it owns behind a zero-egress perimeter, so the data has no outbound path to escape.

https://mickai.co.uk/articles/hedge-funds-sovereign-ai-protect-alpha-mnpi

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Can We Run Online Safety Act Moderation Without Sending Content to Cloud AI?

Yes. Online Safety Act content moderation can run entirely on operator-owned hardware, with no user content or evidence sent to cloud AI. On-device classifiers meet the illegal-harms and child-safety duties while keeping the most sensitive material inside your own jurisdiction.

https://mickai.co.uk/articles/online-safety-act-moderation-on-premise

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Can councils run AI on resident and social care records without adding a cloud data processor?

Councils can run AI on resident and social care records without adding a cloud data processor by running it on hardware they own, with no data leaving the building. If no third party ever receives the records, there is no processor to contract or record.

https://mickai.co.uk/articles/councils-ai-social-care-data-no-processor

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

Can Automotive Suppliers Use AI on OEM Design Data While Keeping TISAX Prototype Protection?

Automotive suppliers can run AI on OEM design and prototype data and keep TISAX prototype protection, but only when the model runs on their own hardware inside the protected zone. Public cloud AI transmits the data outward, which prototype protection forbids.

https://mickai.co.uk/articles/automotive-tisax-prototype-protection-ai

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

How to Prove What an AI System Did After the Fact

You prove what an AI system did after the fact with an offline-verifiable audit ledger: every action is recorded, each entry is sealed with a post-quantum signature, and anyone holding the public key can confirm nothing was altered without trusting the vendor.

https://mickai.co.uk/articles/prove-what-ai-did-offline-verifiable-audit

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

What is the air-gap test, and can your AI pass it?

The air-gap test means physically unplugging the network and checking that the AI still reasons, retrieves and seals its audit record. A system passes only if its models, identity and logs all run on local, operator-owned hardware with zero egress.

https://mickai.co.uk/articles/air-gap-test-can-your-ai-pass-it

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance

How do you keep AI inside the building when the data is regulated?

Keep AI inside the building by running inference and agents on hardware you own, behind a perimeter with no outbound path. Data residency only fixes where files rest. Zero egress fixes whether anything can leave, so regulated data is processed in place and never transmitted.

https://mickai.co.uk/articles/keep-ai-inside-the-building-regulated-data

#SovereignAI #AI #DataSovereignty #PostQuantum #AIgovernance