Update README.md

README.md

@@ -52,26 +52,77 @@ dataset_info:
     num_examples: 3027628
   download_size: 2038476796
   dataset_size: 5265441041
-rai:dataLimitations: |
-  - Coverage is not exhaustive. LOCUS v1.0 does not represent every U.S. municipal or county jurisdiction, and jurisdictions vary in how completely their local law is digitized and codified.
-  - The `function` and `topic` labels are model-assigned and have not been fully human-validated. Label noise should be expected, particularly at taxonomy boundaries (e.g., Rules vs. Process, Buildings vs. Zoning).
-  - The `is_substantive` field is derived deterministically from `function` and inherits any errors in function labeling.
-  - The topic taxonomy is intentionally coarse (Buildings, Business, Nuisance, Zoning, Other) and does not capture fine-grained subject matter important for some downstream tasks.
-  - Header-only rows are retained in the dataset but are not appropriate inputs for substantive classification and are explicitly excluded from model inference.
-  - Local law changes over time. The dataset reflects a snapshot and may not match the current law of any given jurisdiction.
-rai:dataBiases: |
-  - Geographic and population skew: larger and more urbanized jurisdictions, and jurisdictions that publish their codes through major codification vendors, are likely overrepresented relative to small or rural municipalities.
-  - State-level legal regimes differ, so the distribution of functions and topics is not uniform across states; aggregate label frequencies should not be assumed to generalize to any individual jurisdiction.
-  - The taxonomy itself encodes choices about what counts as "substantive" law (Rules and Enforcement) versus procedural, contextual, or structural provisions. Alternative legal-theoretic framings would produce different labels.
-  - Labels were produced by automated classifiers and may reflect biases of those classifiers, including biases inherited from their training data.
-  - English-language only. Non-English text occasionally present in U.S. local codes is not handled as a separate class.
-rai:personalSensitiveInformation: |
-  LOCUS v1.0 is built from publicly enacted municipal and county law, which is a matter of public record. However, local ordinances can incidentally contain references to identifiable individuals (e.g., named officials, sponsors, or parties to specific proceedings) and to specific properties or businesses (e.g., addresses in zoning provisions, license holders, named establishments). No effort has been made to redact such incidental references. The dataset does not contain non-public personal data, biometric data, health data, financial account information, or government-issued identifiers.
-rai:dataUseCases: |
-  Intended uses include legal text classification research, analysis of local-law structure and composition, substantive vs. non-substantive filtering for downstream legal NLP pipelines, refinement of legal taxonomies, and weakly supervised or human-in-the-loop annotation workflows. The dataset is not appropriate as legal advice, as a substitute for human legal review, as a complete census of U.S. local law, or as a fully validated benchmark in the absence of additional human auditing.
-rai:dataSocialImpact: |
-  Anticipated positive impacts include lowering the cost of empirical research on local government law, supporting tools that help residents, journalists, and researchers understand municipal regulation, and enabling reproducible study of how local jurisdictions structure their codes. Anticipated risks include over-reliance on automated classifications in settings with legal consequences, misinterpretation of aggregated label frequencies as authoritative statements about the law, and downstream tools surfacing model outputs to end users as if they were vetted legal information. Users deploying systems built on this dataset should clearly disclose the automated and unaudited nature of the labels and should not present outputs as legal advice.
+rai:dataLimitations: >
+  - Coverage is not exhaustive. LOCUS v1.0 does not represent every U.S.
+  municipal or county jurisdiction, and jurisdictions vary in how completely
+  their local law is digitized and codified.
+
+  - The `function` and `topic` labels are model-assigned and have not been fully
+  human-validated. Label noise should be expected, particularly at taxonomy
+  boundaries (e.g., Rules vs. Process, Buildings vs. Zoning).
+
+  - The `is_substantive` field is derived deterministically from `function` and
+  inherits any errors in function labeling.
+
+  - The topic taxonomy is intentionally coarse (Buildings, Business, Nuisance,
+  Zoning, Other) and does not capture fine-grained subject matter important for
+  some downstream tasks.
+
+  - Header-only rows are retained in the dataset but are not appropriate inputs
+  for substantive classification and are explicitly excluded from model
+  inference.
+
+  - Local law changes over time. The dataset reflects a snapshot and may not
+  match the current law of any given jurisdiction.
+rai:dataBiases: >
+  - Geographic and population skew: larger and more urbanized jurisdictions, and
+  jurisdictions that publish their codes through major codification vendors, are
+  likely overrepresented relative to small or rural municipalities.
+
+  - State-level legal regimes differ, so the distribution of functions and
+  topics is not uniform across states; aggregate label frequencies should not be
+  assumed to generalize to any individual jurisdiction.
+
+  - The taxonomy itself encodes choices about what counts as "substantive" law
+  (Rules and Enforcement) versus procedural, contextual, or structural
+  provisions. Alternative legal-theoretic framings would produce different
+  labels.
+
+  - Labels were produced by automated classifiers and may reflect biases of
+  those classifiers, including biases inherited from their training data.
+
+  - English-language only. Non-English text occasionally present in U.S. local
+  codes is not handled as a separate class.
+rai:personalSensitiveInformation: >
+  LOCUS v1.0 is built from publicly enacted municipal and county law, which is a
+  matter of public record. However, local ordinances can incidentally contain
+  references to identifiable individuals (e.g., named officials, sponsors, or
+  parties to specific proceedings) and to specific properties or businesses
+  (e.g., addresses in zoning provisions, license holders, named establishments).
+  No effort has been made to redact such incidental references. The dataset does
+  not contain non-public personal data, biometric data, health data, financial
+  account information, or government-issued identifiers.
+rai:dataUseCases: >
+  Intended uses include legal text classification research, analysis of
+  local-law structure and composition, substantive vs. non-substantive filtering
+  for downstream legal NLP pipelines, refinement of legal taxonomies, and weakly
+  supervised or human-in-the-loop annotation workflows. The dataset is not
+  appropriate as legal advice, as a substitute for human legal review, as a
+  complete census of U.S. local law, or as a fully validated benchmark in the
+  absence of additional human auditing.
+rai:dataSocialImpact: >
+  Anticipated positive impacts include lowering the cost of empirical research
+  on local government law, supporting tools that help residents, journalists,
+  and researchers understand municipal regulation, and enabling reproducible
+  study of how local jurisdictions structure their codes. Anticipated risks
+  include over-reliance on automated classifications in settings with legal
+  consequences, misinterpretation of aggregated label frequencies as
+  authoritative statements about the law, and downstream tools surfacing model
+  outputs to end users as if they were vetted legal information. Users deploying
+  systems built on this dataset should clearly disclose the automated and
+  unaudited nature of the labels and should not present outputs as legal advice.
 rai:hasSyntheticData: false
+license: cc-by-nc-4.0
 ---
 
 # LOCUS v1.0