Datasets:

vorplabs
/

federal-ai-enforcement

Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 4, saw 2

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/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 2

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US Federal AI Enforcement Tracker

The US Federal AI Enforcement Tracker is a source-backed dataset of US federal enforcement actions and selected adjacent official materials involving artificial intelligence or automated systems.

This Hugging Face repository is a distribution mirror maintained by Vorp Labs. The release preserved here is 2026.07.19, covering 42 tracked federal actions as of 2026-07-18.

Permanent identifiers and canonical sources

Canonical research page: https://vorplabs.com/ai-regulatory-updates/federal-enforcement
Canonical release manifest: https://vorplabs.com/datasets/federal-ai-enforcement/manifest.json
Immutable GitHub release: https://github.com/vorplabs/research-datasets/tree/73c7586aa4df7640000760520fdbcf96d371303c/datasets/federal-ai-enforcement/2026.07.19
Version DOI: https://doi.org/10.5281/zenodo.21456232
Concept DOI: https://doi.org/10.5281/zenodo.21456231

The canonical Vorp Labs page remains the source of truth for methodology, scope, limitations, corrections, and newer releases. This repository preserves a convenient Hugging Face mirror of the cited version.

Repository layout

The complete immutable release package is stored under releases/2026.07.19/:

federal-ai-enforcement.csv: flat action table for spreadsheets and analysis.
federal-ai-enforcement.json: structured release object with action records, adjacent materials, change log, and monitoring sources.
release-manifest.json: version, source URLs, record counts, byte counts, and canonical SHA-256 checksums.
CITATION.cff: machine-readable citation metadata.
README.md: the release-specific documentation preserved byte-for-byte.
SHA256SUMS: package integrity checks for all other release files.
LICENSE.txt: CC BY 4.0 attribution terms for Vorp-created data and documentation.

Primary action fields

anchor, agency, target, and shortName identify an action.
filedDate, orderDate, and their precision fields preserve date uncertainty.
legalAuthority, status, outcome, summary, and aiIssueTags describe the matter.
docketOrFileNumbers, events, penalties, and relatedAnchors support cross-checking.
factChecks, openItems, sources, and url preserve evidence and record-level provenance.

Use and limitations

The tracker is descriptive research, not legal advice, and it does not claim that every enforcement matter involving software or algorithms is included. Check the primary-source links and the as-of date before relying on a record.

Integrity verification

From the release directory, run:

shasum -a 256 -c SHA256SUMS

The data and documentation are released under CC BY 4.0. See the release package for the complete attribution terms.

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