GPS-bench/gps-bench-ai-bills

GPS-Bench AI Legal Instruments Dataset

An evidence-backed corpus of global AI-related laws, regulations, policies, guidelines, standards, and strategic instruments. Each canonical record retains its official title, issuing authority, normalized jurisdiction, legal status, dates, primary-source citations, and full verified text. Every non-English instrument keeps its source-language original as the authoritative text, with an English translation beside it produced by a single translator, gemini-3.5-flash. The companion benchmark view projects instruments evaluated against expert human analyses.

This dataset card documents the canonical tables, evaluation benchmark splits, querying patterns, and schema invariants for the Hugging Face Hub release.

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Primary Corpus vs. Benchmark View

The dataset is published with two complementary tables designed for distinct use cases:

Table / Config	Primary Use Case	Description
instruments (instruments.jsonl)	Global Policy Research & Full-Text Retrieval	Complete corpus of global AI laws, bills, regulations, guidelines, and standards with full primary texts, translations, timelines, and citations.
benchmark_view (benchmark_view.jsonl)	LLM Evaluation & Legal Comprehension Benchmarking	Benchmark evaluation projection containing only instruments paired with curated human expert legal/policy analyses.

Record counts live in manifest.json, not on this card. The build regenerates that file and publish_to_hf.py gates it against the shipped rows; a count typed into prose has nothing checking it, and ages silently every time an instrument or analysis is ingested. Read counts from the manifest, or from the tables themselves.

Evaluation Splits in benchmark_view.jsonl

Each benchmark row maps an instrument to its evaluation split:

• before_cutoff: Pre-cutoff enactments with $\ge 2$ expert analyses, for standard policy comprehension evaluation.
• after_cutoff: Post-cutoff instruments, evaluated against models without data contamination.
• train: Held-out training split and few-shot in-context exemplar set.
• one_analysis: Instruments accompanied by a single verified expert legal commentary. At least one instrument in this split carries two analyses rather than one — read analysis_count rather than assuming the split name.

Group by split on benchmark_view.jsonl for the current size of each.

Both tables are guaranteed to remain in 100% lockstep: the automated build (make build) and validation (make validate / publish_to_hf.py) pipelines verify that every instrument carrying expert analyses is synchronized with benchmark_view.jsonl before release.

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Instruments the Corpus Does Not Actually Hold

A census on 2026-08-20, extended on 2026-08-22, read the captured text of every record and found 57 instruments whose document is not the instrument it claims to be, across 69 documents. Each is flagged in place: documents[].extraction_provenance.cleaning_actions carries the class, defect_note says what is wrong in plain language, and defect_basis records the measured evidence and which census pass found it. Nothing was deleted, re-fetched or repaired — a flag is a statement that somebody looked.

The second pass found six the first missed, and they divide the same way: three hold the wrong document outright (an Innovation Zurich news index standing for the Canton of Zurich sandbox; a board-institute article standing for the SDAIA principles; a Hawai'i committee vote history standing for SB 2687), and three are the real text wrapped in whitehouse.gov or Algoritmekader site chrome. The first census scored records on one structural signal; these six were classed by reading the captured text, which is why they took a second pass to see.

Separately: control bytes are gone, and lossy decodes are not. PDF extraction emitted the page structure of a document as C0 control bytes — U+000C between pages, U+0007 and U+0008 where a glyph table did not map, and NUL. They are invisible in a terminal, survive JSON, and reach a tokenizer as garbage, which matters because people train on this. They are now normalised at extraction (ingestion.primary_text.normalise_controls: page and record separators become newlines, every other control byte is dropped) and the existing corpus was swept; validation/audit_text_quality.py fails if any return.

U+FFFD is deliberately not repaired. It marks a byte that failed to decode — Ar<?>cle for "Article", de<?>ne for "define", O<?>Neal for "O'Neal" — and the character it replaced is unrecoverable. Guessing would put invented words into legal text. The affected documents are counted as lossy_decode_documents by that audit so they can be re-extracted from source.

Class	Instruments	What was captured instead	Recoverable?
text absent (flagged_wrong_document_not_instrument, flagged_announcement_page_not_instrument)	30	bill-status pages, vote and amendment histories, landing pages, bot walls, download stubs, news items announcing the instrument	no — needs a re-fetch, not always available
mangled (flagged_mangled_extraction)	3	the real text, extracted one word per line ("House\nFile\n2240"). All three Iowa	yes, by re-extraction
polluted (flagged_boilerplate_polluted)	24	the real instrument text wrapped in site navigation and cookie boilerplate	yes, by trimming

What this does and does not corrupt. It corrupts the corpus's claim to hold these 51 instruments. It mostly does not corrupt what was mined from them: a navigation page contains no normative language, so the miner finds nothing in it. The defect is largely self-limiting — most text-absent instruments yield zero clauses, and gps:cn-na-pdngaxx-2017, the worst on every signal, yields zero.

The headline number to resist: roughly 3% of the statutory clauses in GPS-bench/actor come from a flagged instrument — but that figure is dominated by the polluted class, whose clauses are genuine. Only a couple of dozen clauses come from a text-absent or mangled instrument, and hand-reading every one of those put the actually-junk figure at 8 rows — four of them the literal site string "Links to said data may not be functional at this time." mined as a prohibition.

You can check this yourself: the actor layer's bill_key is this dataset's instrument_id without its gps: prefix, and the mapping is total in both directions.

To exclude flagged records:

FLAGS = {
    "flagged_wrong_document_not_instrument",
    "flagged_announcement_page_not_instrument",
    "flagged_mangled_extraction",
}  # add "flagged_boilerplate_polluted" only if navigation text would harm your task

def holds_its_instrument(record):
    return not any(
        set((d.get("extraction_provenance") or {}).get("cleaning_actions") or []) & FLAGS
        for d in record.get("documents") or []
    )

---

Dataset Overview & Geopolitical Scopes

The dataset classifies legal instruments across four mutually exclusive geopolitical scopes (entity_context.scope):

Scope (entity_context.scope)	Description	Example Entities & Jurisdictions
us	United States federal and subnational legislation and executive instruments.	US Congress, White House, California (US-CA), New York (US-NY), Tennessee (US-TN).
china	National-level and provincial Chinese laws, regulations, and white papers.	State Council, CAC, MIIT, Guangdong Province, Beijing Municipality.
other_countries	Sovereign nation-states and their subnational territories outside the US and China.	United Kingdom (GB), Canada (CA), Japan (JP), Singapore (SG), India (IN), Brazil (BR), Chile (CL), etc.
multilateral	Multilateral bodies, intergovernmental alliances, international standard-setters, and supranational unions.	European Union (EU), ASEAN, African Union (AFRUNION), United Nations (UN), OECD, G7, Council of Europe (COE), GPAI, BRICS, Canada–EU MoU.

Jurisdiction Kinds (jurisdiction_kind)

Every instrument is issued under a specific jurisdiction_kind:

• country: Sovereign nation-state (e.g. US, CN, GB, CA, JP, SG).
• subnational: Subordinate territorial jurisdiction, such as a US state or Canadian province (e.g. US-CA, US-TN, CA-ON).
• supranational: Reserved exclusively for the European Union (EU), reflecting its unique supranational legislative powers directly enforceable across member states.
• multilateral: Intergovernmental organizations, international treaties, declarations, and regional/bilateral partnerships (e.g. ASEAN, African Union, UN, OECD, G7, COE, GPAI, BRICS, Canada–EU MoU).

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Release Structure & Contents

datasets/bill_analysis_dataset/
├── hf/                                  # Canonical published release
│   ├── instruments.jsonl                # Primary corpus: 1 row per canonical instrument
│   ├── benchmark_view.jsonl             # Benchmark projection: instruments paired with analyses
│   ├── schema/
│   │   ├── instruments.schema.json      # Authoritative JSON Schema for instruments.jsonl
│   │   └── benchmark_view.schema.json   # Authoritative JSON Schema for benchmark_view.jsonl
│   ├── manifest.json                    # Build metadata, sha256 checksums, and record counts
│   └── README.md                        # Hub dataset card (synced from root README.md)
├── mappings/                            # Controlled vocabularies and host mappings
│   ├── jurisdiction_mapping.yaml        # Organization codes, names, and country aliases
│   ├── source_type_mapping.yaml         # URL domain classification rules
│   └── status_mapping.yaml              # Regex pattern rules for status normalization
├── ingestion/                           # Ingestion tools (URL fetching, PDF parsing, translation)
│   ├── add_instrument.py                # fetch and add CLI subcommands
│   ├── primary_text.py                  # HTML/PDF cleaners and language detector
│   ├── refresh_summaries.py             # Generates grounded ai_relevance summaries
│   └── sync_release.py                 # Merges verified additions and syncs hf/ release
├── validation/                          # Integrity and quality audit scripts
│   ├── validate_dataset.py              # Cross-field relational and schema validator
│   ├── audit_text_quality.py            # Checks for tag leaks, boilerplate, and text lengths
│   └── deduplicate_dataset.py           # Multi-heuristic duplicate detector
├── schema.py                            # Pydantic v2 source of truth for all schemas
├── Makefile                             # Ingestion and build target orchestrator
└── README.md                            # Canonical documentation (this file)

---

Canonical Record Schema

The principal top-level fields in hf/instruments.jsonl are:

Field	Type	Description
instrument_id	string	Unique stable identifier (e.g. gps:eu-ai-act, gps:ca-ab-2013-2024).
title_official	string	Official English title, or verified English translation of the instrument title.
title_original	string | null	Native original title in its original language (if non-English).
title_original_language	string | null	ISO language code of original title (e.g. zh, es, de, fr).
jurisdiction	string	Human-readable issuing jurisdiction (e.g. European Union, Canada, US-CA).
jurisdiction_kind	enum	country, subnational, supranational (EU only), or multilateral.
country_code	string | null	ISO 3166-1 alpha-2 code (US, CN, GB, CA, etc.) or null for multilateral bodies.
subdivision_code	string | null	ISO 3166-2 code for states/provinces (e.g. US-CA, US-TX) or null.
organization_code	string | null	Code for multilateral/supranational organizations (EU, ASEAN, UN, OECD, etc.).
entity_context	object	`{ "scope": "us"
issuing_body	string | null	Government ministry, parliament, or organization branch that issued the instrument.
instrument_type	enum | null	act, bill, regulation, decree, policy, guideline, standard, treaty, resolution, code, report, speech, other.
status_raw	string	Verbatim status reported by the primary source or gazette.
status_class	enum	Normalized status: enacted, active, draft, guidance, stalled, other.
normative_character	enum	Normalized legal force: hard_law, soft_law, or non_instrument.
primary_language	string	ISO language code of primary text (en, zh, es, fr, ja, etc.).
ai_relevance	string | null	Source-grounded concise summary of AI provisions and obligations.
dates	list[object]	Structured timeline of events (event_type, event_date, date_precision, date_basis).
documents	list[object]	Full text payloads (text, language, document_role, sha256, extraction_provenance).
sources	list[object]	Official citations and status evidence links (url, publisher, is_official, source_class).
analyses	list[object]	Citable expert legal/policy commentaries attached to this instrument.
external_identifiers	list[object]	Authentic external legal/official identifiers (scheme, value).
annotations	list[object]	Derived research labels (such as V-Dem regime classification or task tags).

---

Loading & Querying the Dataset

Using Python & Hugging Face datasets

from datasets import load_dataset

# Load canonical legal instruments
ds = load_dataset("GPS-bench/gps-bench-ai-bills", "instruments", split="train")

# 1. Filter by jurisdiction kind (country, subnational, supranational, multilateral)
countries_only = ds.filter(lambda row: row["jurisdiction_kind"] == "country")
multilateral_only = ds.filter(lambda row: row["jurisdiction_kind"] == "multilateral")

# 2. Filter by geopolitical scope (us, china, other_countries, multilateral)
other_countries = ds.filter(lambda row: row["entity_context"]["scope"] == "other_countries")
us_instruments = ds.filter(lambda row: row["entity_context"]["scope"] == "us")

# 3. Filter by specific country code (ISO 3166-1 alpha-2)
uk_bills = ds.filter(lambda row: row["country_code"] == "GB")
canada_bills = ds.filter(lambda row: row["country_code"] == "CA")

# 4. Combined filter: Enacted national laws across select countries
target_countries = {"CA", "GB", "JP", "SG", "AU"}
enacted_laws = ds.filter(
    lambda row: row["country_code"] in target_countries
    and row["jurisdiction_kind"] == "country"
    and row["status_class"] == "enacted"
    and row["normative_character"] == "hard_law"
)

# Load benchmark view (instruments paired with human analyses)
benchmark = load_dataset(
    "GPS-bench/gps-bench-ai-bills", "benchmark_view", split="train"
)

High-Performance Querying with Polars

import polars as pl

df = pl.read_ndjson("hf/instruments.jsonl")

# 1. Filter by jurisdiction_kind
subnational_df = df.filter(pl.col("jurisdiction_kind") == "subnational")
multilateral_df = df.filter(pl.col("jurisdiction_kind") == "multilateral")

# 2. Filter by geopolitical scope (entity_context.scope)
other_countries_df = df.filter(
    pl.col("entity_context").struct.field("scope") == "other_countries"
)

# 3. Filter by country code (single or multiple ISO codes)
uk_df = df.filter(pl.col("country_code") == "GB")
g7_df = df.filter(pl.col("country_code").is_in(["US", "GB", "CA", "FR", "DE", "IT", "JP"]))

# 4. Combined filter: Hard-law enactments outside US & China with summaries
filtered = (
    df.filter(
        (pl.col("entity_context").struct.field("scope") == "other_countries")
        & (pl.col("jurisdiction_kind") == "country")
        & (pl.col("normative_character") == "hard_law")
        & (pl.col("status_class") == "enacted")
    )
    .select(["instrument_id", "title_official", "country_code", "status_class", "ai_relevance"])
)
print(filtered)

# 5. Summary statistics: count instruments by scope and status
summary = (
    df.group_by(
        [
            pl.col("entity_context").struct.field("scope").alias("scope"),
            pl.col("jurisdiction_kind"),
            pl.col("status_class"),
        ]
    )
    .agg(pl.len().alias("count"))
    .sort(["scope", "count"], descending=[False, True])
)
print(summary)

Memory-Efficient Streaming with Standard Python

import json

with open("hf/instruments.jsonl", encoding="utf-8") as f:
    for line in f:
        row = json.loads(line)
        scope = row["entity_context"]["scope"]
        kind = row["jurisdiction_kind"]
        country = row["country_code"]

        # Filter for non-US/China sovereign country laws
        if scope == "other_countries" and kind == "country" and country == "JP":
            print(f"Japan: {row['instrument_id']} — {row['title_official']}")
        elif kind == "supranational":
            print(f"Supranational: {row['instrument_id']} ({row['title_official']})")
        elif kind == "multilateral":
            print(f"Multilateral:  {row['instrument_id']} ({row['jurisdiction']})")

---

Jurisdiction & Scope Invariants

When querying, filtering, or contributing new instruments, keep these core invariants in mind:

1. Supranational vs. Multilateral:
   • jurisdiction_kind == "supranational" is strictly limited to the European Union (EU).
   • All other international organizations (ASEAN, African Union, G7, OECD, UN, GPAI, Council of Europe, BRICS, bilateral MoUs) must use jurisdiction_kind == "multilateral".
2. Entity Scope (entity_context.scope):
   • us: US federal + state/local instruments.
   • china: China national + provincial instruments.
   • other_countries: Sovereign nations and territories (UK, Canada, Japan, etc.).
   • multilateral: Multilateral organizations and the EU.
3. Regime Annotations:
   • Multilateral and supranational entities receive regime: "NA" with the basis "Not applicable to a supranational or multilateral entity." because V-Dem democracy indices only evaluate sovereign nation-states.

---

Translation Provenance

Non-English instruments carry two documents: the source-language original, which is the authoritative text, and an English rendering beside it. The English rendering is never the instrument — it exists for search, retrieval, and analysis.

Every machine translation in this release was produced by one model, gemini-3.5-flash. Earlier revisions accumulated renderings from six different models, because each ingestion pass used whichever model that pass happened to reach for. That is a benchmark defect rather than a cosmetic one: when the English text of two instruments comes from two different translators, a disagreement measured downstream cannot be attributed to the instruments rather than to their translators. Re-rendering the whole corpus through a single translator removes that confound.

Document kind	translation_method	extraction_provenance.tool
Machine translation	machine	gemini-3.5-flash
Official published translation	official	publisher-specific

Counts are in manifest.json, not here — this card cannot be regenerated and its numbers went stale once already.

The model is recorded in extraction_provenance.tool, which is the field the schema reserves for it — ExtractionProvenance is closed (additionalProperties: false) and has no model field. Read the translator from tool, not from note.

Analyses carry the same distinction, in analyses[].full_text_en_machine. An analysis published in Latvian, Danish, Spanish or French keeps its excerpt and full_text in the source language — the quote gate re-fetches the cited page and requires the excerpt verbatim, so a translated excerpt would be refused, and rightly: it is not what the page says. The English rendering travels beside them for the readers downstream that only have English lexicons. Measured 2026-08-22, a Latvian analysis of a real 2024 deepfake statute produced nothing at all until it had one. As with instrument text, the translation is model output and is never the authority for what the analysis says.

The two official documents are English texts published by the issuing government itself (Israel's AI Policy 2023 and Government Resolution 173). They are not model output and are deliberately excluded from re-translation. Note that Israel's AI Policy states on its own page 3 that it is an English summary of the Hebrew policy, not a full translation; the Hebrew remains the complete instrument.

Machine translations cover 18 source languages, most commonly Spanish (18 instruments) and Chinese (11). Run make audit-models to reproduce the table above from the release itself.

---

Ingestion Pipeline: Adding New Instruments

Ingesting new primary sources uses a strict two-step URL-first workflow via ingestion/add_instrument.py:

Step 1: Fetch & Inspect (fetch)

make ingest ARGS="fetch --url 'https://example.gov/official-ai-act'"

• Downloads the official source (HTML or PDF via Poppler).
• Automatically detects language and generates a checkpointed gemini-3.5-flash English translation if non-English.
• Performs duplicate detection against existing canonical records.
• Caches raw output under inputs/raw/fetched/<slug>.json for human inspection.

Step 2: Build & Validate (add)

Review the fetched text and run add (dry run first, then --write):

make ingest ARGS="add \
  --url 'https://example.gov/official-ai-act' \
  --instrument-id gps:ca-act-2026 \
  --title 'Official English Title' \
  --issuer 'Ministry of Industry' \
  --jurisdiction 'Canada' \
  --jurisdiction-kind country \
  --country-code CA \
  --instrument-type act \
  --status-raw 'Royal Assent' \
  --status-class enacted \
  --normative-character hard_law \
  --issued-date 2026-03-01 \
  --ai-relevance 'Establishes safety testing duties for frontier AI models.' \
  --write"

For multilateral or non-country instruments:

make ingest ARGS="add \
  --url 'https://asean.org/example-guide.pdf' \
  --instrument-id gps:asean-sample-guide-2026 \
  --title 'ASEAN Sample AI Guide' \
  --issuer 'ASEAN Secretariat' \
  --jurisdiction 'Association of Southeast Asian Nations' \
  --jurisdiction-kind multilateral \
  --organization-code ASEAN \
  --instrument-type guideline \
  --status-raw 'Adopted' \
  --status-class guidance \
  --normative-character soft_law \
  --issued-date 2026-05-10 \
  --ai-relevance 'Regional framework for trustworthy AI deployment.' \
  --write"

---

Validation & Reproducibility

To maintain integrity across the release, standard Makefile targets are provided:

make build       # Synchronizes verified additions from inputs/ into hf/ and updates README
make validate    # Validates cross-field schemas, unique IDs, and entity context
make audit-text  # Audits document completeness, HTML leaks, and translation coverage
make test        # Runs full test suite (validate + audit-text)
make actor       # Rebuilds actor relationship graph and explorer map
make explorer    # Rebuilds the standalone HTML Bill Explorer
make deduplicate # Runs multi-heuristic duplicate check across the corpus
make summaries   # Regenerates grounded English summaries for verified additions

---

Changelog

Data revision 2026-08-21

• Defective captures flagged, not hidden: a census read every captured document and flagged 51 instruments whose text is not the instrument they claim to hold — 27 absent, 3 mangled, 21 boilerplate-polluted. See Instruments the Corpus Does Not Actually Hold. extraction_provenance gained defect_note and defect_basis to carry the finding and its evidence.
• Benchmark view grew substantially, as newly ingested expert analyses were paired with their instruments. Every split changed size. Hard counts have since been removed from this card in favour of manifest.json, which the build keeps current.

Data revision 2026-08-20

• US federal AI executive orders completed: added EO 14110 (Safe, Secure, and Trustworthy Development and Use of AI, 2023), EO 13960 (Trustworthy AI in the Federal Government, 2020) and EO 14141 (AI Infrastructure, 2025) with full Federal Register text. The corpus previously jumped from EO 13859 (2019) straight to 2025, leaving the whole 2020-2024 span of US executive action on AI unrepresented.
• Single translator across the corpus: re-rendered every machine translation with gemini-3.5-flash, replacing renderings previously produced by gpt-4.1, deepseek-v4-pro, claude-sonnet-4-5, and gemini-3.6-flash. The model is now recorded uniformly in extraction_provenance.tool.
• Vietnam AI Law rebuilt from the Official Gazette: gps:vn-ai-law-2025 previously held an OCR of a scanned signature PDF plus an English scrape of a commercial legal database that was mislabelled official. Both were replaced from the Công báo DOCX (Articles 1–35, Chapters I–VIII).
• Record counts corrected: the card had been understating the release, and the counts it carried had drifted from the build. They now live in manifest.json instead.

Version 2.3.0 (2026-08)

• Updated Geopolitical Scopes: Standardized entity_context.scope to four mutually exclusive scopes: us, china, other_countries, and multilateral.
• Multilateral Jurisdiction Kind: Renamed intergovernmental to multilateral under jurisdiction_kind.
• EU Supranational Rule: Restated supranational exclusively for European Union (EU) instruments, classifying all other international bodies (ASEAN, African Union, UN, OECD, G7, COE, GPAI, BRICS, bilateral MoUs) under multilateral.
• Verified Primary Sources: Added verified primary text extractions, schema validation rules, and comprehensive dataset documentation.