Datasets:

Crovia
/

global-ai-training-omissions

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
schema: string
generated_at: double
context: struct<what_this_is: string, what_this_is_not: string>
start_here: struct<ranking_jsonl: string, canon: string, snapshot: string>
top_omissions: list<item: struct<necessity_id: string, score: double, avg_persistence_days: double, records: int64>>
vs
schema: string
generated_at: string
count: int64
badges: list<item: struct<model_id: string, score: int64, badge: string, card_path: string, badge_path: string>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 531, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              schema: string
              generated_at: double
              context: struct<what_this_is: string, what_this_is_not: string>
              start_here: struct<ranking_jsonl: string, canon: string, snapshot: string>
              top_omissions: list<item: struct<necessity_id: string, score: double, avg_persistence_days: double, records: int64>>
              vs
              schema: string
              generated_at: string
              count: int64
              badges: list<item: struct<model_id: string, score: int64, badge: string, card_path: string, badge_path: string>>

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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🔮 OMISSION ORACLE

We see what they don't disclose.

🔮 Live Scanner · 📊 Leaderboard · 🏅 Get Badge

⚠️ WHAT WE TRACK

┌────────────────────────────────────────────────────────────────────────────┐
│                                                                            │
│   Every 6 hours, the Oracle scans the most popular AI models.              │
│   We detect what's MISSING — not what's claimed.                           │
│                                                                            │
│   📊 No data provenance?     We see it.                                    │
│   📜 No license declared?    We see it.                                    │
│   🎯 No usage scope?         We see it.                                    │
│   👤 No accountable entity?  We see it.                                    │
│                                                                            │
│   The results are PUBLIC. The badges are AUTOMATIC.                        │
│   There is nowhere to hide.                                                │
│                                                                            │
└────────────────────────────────────────────────────────────────────────────┘

🏅 Embed Your Trust Badge

Add a Crovia Trust Badge to your model's README:

![Crovia Trust](https://huggingface.co/datasets/Crovia/global-ai-training-omissions/resolve/main/badges/YOUR_MODEL.svg)

Example Badges

Model	Badge	Score
google/gemma-2-9b	🟢 GOLD	100/100
mistralai/Mistral-7B	🟢 GOLD	94/100
microsoft/Phi-3-mini	🟢 GOLD	90/100
meta-llama/Llama-3-8B	🟡 SILVER	77/100
bigscience/bloom	🔴 UNVERIFIED	59/100

📊 Shadow Score Leaderboard

The Shadow Score measures trust declaration completeness (0-100):

Score	Badge	Meaning
≥90	🟢 GOLD	Fully compliant - all declarations present
≥75	🟡 SILVER	Minor gaps - mostly compliant
≥60	🟠 BRONZE	Needs attention - significant gaps
<60	🔴 UNVERIFIED	Critical - major declarations missing

🚨 Top Omissions Detected

NEC#	Violation	Records	Severity
NEC#2	Missing license attribution	111	🔴 Critical
NEC#13	Missing accountable entity	108	🔴 Critical
NEC#10	Missing temporal validity	46	🟡 Medium
NEC#1	Missing data provenance	32	🔴 Critical
NEC#7	Missing usage scope	18	🟡 Medium

🔬 How It Works

┌─────────────────────────────────────────────────────────────────┐
│                    OMISSION ORACLE PIPELINE                     │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. OBSERVE           2. ANALYZE           3. PUBLISH          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐      │
│  │ HuggingFace  │───▶│ NEC# Canon   │───▶│ Oracle Cards │      │
│  │ Model Cards  │    │ Matching     │    │ SVG Badges   │      │
│  │ API Scan     │    │ Shadow Score │    │ Rankings     │      │
│  └──────────────┘    └──────────────┘    └──────────────┘      │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

📁 Dataset Structure

├── EVIDENCE.json              # Main evidence with top omissions
├── badges/                    # 🆕 SVG badges for embedding
│   ├── meta-llama__Llama-3-8B.svg
│   └── index.json
├── cards/                     # 🆕 Oracle Cards (JSON)
│   ├── meta-llama__Llama-3-8B.json
│   └── ...
├── canon/
│   └── necessities.v1.yaml    # NEC# definitions (20 types)
├── open/
│   ├── forensic/              # Analysis engines
│   │   ├── hubble_continuum.py
│   │   └── badge_generator.py # 🆕 Badge generator
│   ├── signal/                # Presence/absence signals
│   └── temporal/              # Historical pressure data
└── v0.1/                      # Versioned snapshots

⚡ Live Scanner

Launch CEP Terminal →

Enter any HuggingFace model ID and get instant trust analysis:

Shadow Score calculation
NEC# violation detection
Badge recommendation
Evidence hash

🔗 Integration

For Model Maintainers

Add this to your model's README to show your trust score:

[![Crovia Trust Score](https://huggingface.co/datasets/Crovia/global-ai-training-omissions/resolve/main/badges/YOUR_ORG__YOUR_MODEL.svg)](https://huggingface.co/spaces/Crovia/cep-terminal)

For CI/CD Pipelines

# Check model trust before deployment
curl -s https://huggingface.co/datasets/Crovia/global-ai-training-omissions/resolve/main/cards/meta-llama__Llama-3-8B.json | jq '.shadow_score'

What this dataset is

Crovia — Global Ranking of AI Training Omissions (Hubble) v0.1

This dataset publishes verifiable, hash-anchored evidence of persistent omissions observed across widely used AI training datasets.

It answers one and only one question:

Are public AI training disclosures observable — yes or no?

This dataset IS

an observation layer, not an audit
a cryptographically verifiable record
a public, reproducible signal of absence or presence
aligned with EU AI Act transparency principles

This dataset IS NOT

it does not audit models
it does not infer intent
it does not assign blame
it does not make legal claims

Automatic, observable updates

This dataset updates automatically:

public artifacts are observed on a fixed schedule
if nothing changes, the update itself proves persistence
if something changes, hashes and commits change accordingly
no manual curation
no retroactive edits

Every update is:

publicly committed
reproducible
independently verifiable

Start here (viewer-first)

If you open only one file, open:

➡️ START_HERE.md

It explains the evidence layout for non-technical readers.

Open Plane (public observation layer)

The Open Plane measures one condition only:

Absence is observable.

It contains:

presence signals:
open/signal/presence_latest.jsonl
absence receipts (time-bucketed):
open/forensic/absence_receipts_7d.jsonl
overview:
open/README.md

Core artifacts

ranking (human-readable): global_ranking.jsonl
current snapshot: snapshot_latest.json
cryptographic proof: EVIDENCE.json
canonical vocabulary: canon/necessities.v1.yaml

PRO Shadow (non-disclosing)

Crovia PRO can compute private semantic measurements.

The Open Plane publishes a hash-anchored shadow pointer proving that a measurement exists without disclosing private data:

open/signal/pro_shadow_pressure_latest.json
open/README_PRO_SHADOW.md

Temporal pressure (silence over time)

Crovia tracks how long silence persists under sustained observation.

Temporal pressure increases when:

observation coverage is HIGH
no public training evidence is disclosed
silence persists across days

This does not imply wrongdoing.

➡️ open/temporal/temporal_pressure_30d.jsonl

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