Cards, Provenance, Regulation and Welfare

EU AI Act dates, four regimes, three cards, AB 2013 fields, welfare EV and the external evaluators.

Four regimes side by side#

Factor	EU	US	UK	Korea
Body or law	EU AI Act	CAISI under NIST	AI Security Institute	AI Framework Act
Key date	In force 1 August 2024	Renamed June 2025	Renamed February 2025	Effective January 2026
Posture	Strict, risk-tiered	Pro-growth	Narrow security focus	MSIT-led
Main duty	Risk tiers, Article 50 labels	Standards and innovation	Frontier capability security	Local representatives for foreign AI companies
Extra	Penalties up to 15M EUR or 3%	States fill federal gaps	Open-sourced Inspect	Consolidates 19 AI bills
Signal	Benchmark for privacy-adjacent regulation	Less pre-deployment evaluation	Drops bias and free-speech framings	Article 12 AISI under MSIT

AB_2013_FIELDS = [

"sources_or_owners",1

"how_dataset_furthers_intended_purpose",2

"number_of_data_points (or range)",3

"types_of_data_points (label types or general characteristics)",4

"purchased_or_licensed (Y/N)",6

"cleaning_processing_or_modification_description",9

"dates_first_used_during_development",11

"uses_synthetic_data_generation (Y/N)",12, new vs datasheets

]

5 copyright flag: respect EU TDM opt-out

7 personal information flag: triggers CPRA obligations

8 aggregate consumer information, 10 collection time period

Setting	Default	Note
End-conversation cost	0.002	USD per conversation in the L19 code
Moral-patienthood p	0.01	Medium 0.10, high 0.50
Cards with ethical considerations	0.3%	Hugging Face audit, Oreamuno et al. 2023
Download lift of detailed cards	up to 29%	Liang et al. 2024
AB 2013 posting date	January 1, 2026	For systems released on or after January 1, 2022
Top sources with new restrictions	about 25%	2023 -> 2024, Consent in Crisis
MATS scale	527+ researchers	180+ papers, h-index 47
MATS alumni in safety	~80%	Pre-2025 alumni

Symptom	Cause	Fix
Erasure request for data in the weights	Surgical erasure is not possible	Comply at collection time. Unlearning is partial
Dataset holds personal information	AB 2013 item 7	Meet CPRA obligations
Dataset holds copyright material	AB 2013 item 5	Respect EU TDM opt-out signals
Card claim has no proof	Card carries a claim only	Add a verifiable attestation (Laminator)
Welfare self-report follows the question	Self-reports track user expectations	Use behaviour, model organisms and probes
Lab evaluates its own model	Structural conflict of interest	Add external evaluators
Deployment spans several jurisdictions	Rules differ by jurisdiction	Comply with the strictest, typically the EU AI Act
Model card is placeholder-only	Weak sections	Add disaggregated quantitative analysis

Prohibited (Article 5): social scoring, real-time remote biometric identification in public.
High-risk (Annex III): employment, education, credit, law enforcement, justice, migration.
High-risk duties: conformity assessment, risk management, logging, transparency.
GPAI: all GPAI providers have obligations from 2 August 2025.
Systemic-risk GPAI: above 1e25 FLOP training compute. Estimated 5-15 companies.
Limited-risk: Article 50 labels for AI-generated content.
Code chapters: Transparency and Copyright bind all GPAI. Safety and Security binds systemic-risk GPAI.

Command	What it does
`python3 code/main.py` (L19)	Prints EV and INVEST or skip per intervention
`python3 code/main.py` (L24)	Prints the regulatory `TIMELINE`
`python3 code/main.py` (L26)	Prints a model card, a datasheet, a system card
`python3 code/main.py` (L27)	Prints the 12-item AB 2013 summary
`flag_followups(summary)`	Lists follow-up obligations that fields trigger
`render_markdown(summary)`	Writes `(missing)` for an empty field
`python3 code/main.py` (L28)	Prints the `ECOSYSTEM` map of five organisations

def ev(intervention, scenario):

return (intervention.benefit_if_welfare_matters

* scenario.moral_patienthood_probability

- intervention.cost_usd_per_conversation)

verdict = "INVEST" if v > 0 else "skip"invest iff EV > 0

Intervention("end-conversation on extreme edge cases", 0.002, 1.0)

Intervention("shutdown deployed model", 1000.0, 2.0)

Scenario("low moral-patienthood probability", 0.01)

Scenario("high moral-patienthood probability", 0.50)

Use a Model Card to document the model: intended use, factors, metrics, disaggregated analyses.
Use a Datasheet to document the data: motivation, composition, collection, labeling, uses, distribution, maintenance.
Use a Data Card when readers differ: telescopic for non-experts, microscopic for auditors.
Use a System Card for the deployment: security, prompt-injection protection, data-exfiltration detection, incident response.
Add sustainability fields for carbon, water and compute-energy footprint.

GPAI: General-Purpose AI. Large foundation models.
CAISI: Center for AI Standards and Innovation. Renamed from the US AISI.
MSIT: Ministry of Science and ICT. Hosts the Korean AISI.
TDM: text-and-data-mining. EU Copyright Directive exception with opt-out.
CPRA: Privacy Rights Act. AB 2013 item 7 triggers it.
MATS: ML Alignment & Theory Scholars. Mentorship and talent pipeline.
METR: Model Evaluation and Threat Research. Task-based capability evaluations.
Opt-out signal: robots.txt, C2PA "No AI Training," TDM.Reservation.

Is model welfare a consciousness claim?: No. It is an expected-value claim under moral uncertainty.
What did Claude Opus 4 and 4.1 ship?: They can end a conversation in extreme edge cases.
What is the spiritual bliss attractor?: Pairs of Claude instances converge on meditative euphoria.
Does legitimate interest need consent?: No. Public first-party content with opt-out is enough in the 2025 DPA view.
Who does what outside the labs?: Redwood: AI Control. Apollo: scheming evals. Eleos: welfare evals.
Why external evaluation?: The multi-org structure is the quality control.