--- language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: eoir_privacy source_datasets: [] task_categories: - text-classification viewer: false --- # Dataset Card for eoir_privacy ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This dataset mimics privacy standards for EOIR decisions. It is meant to help learn contextual data sanitization rules to anonymize potentially sensitive contexts in crawled language data. ### Languages English ## Dataset Structure ### Data Instances { "text" : masked paragraph, "label" : whether to use a pseudonym in filling masks } ### Data Splits train 75%, validation 25% ## Dataset Creation ### Curation Rationale This dataset mimics privacy standards for EOIR decisions. It is meant to help learn contextual data sanitization rules to anonymize potentially sensitive contexts in crawled language data. ### Source Data #### Initial Data Collection and Normalization We scrape EOIR. We then filter at the paragraph level and replace any references to respondent, applicant, or names with [MASK] tokens. We then determine if the case used a pseudonym or not. #### Who are the source language producers? U.S. Executive Office for Immigration Review ### Annotations #### Annotation process Annotations (i.e., pseudonymity decisions) were made by the EOIR court. We use regex to identify if a pseudonym was used to refer to the applicant/respondent. #### Who are the annotators? EOIR judges. ### Personal and Sensitive Information There may be sensitive contexts involved, the courts already make a determination as to data filtering of sensitive data, but nonetheless there may be sensitive topics discussed. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to learn contextual privacy rules to help filter private/sensitive data, but itself encodes biases of the courts from which the data came. We suggest that people look beyond this data for learning more contextual privacy rules. ### Discussion of Biases Data may be biased due to its origin in U.S. immigration courts. ### Licensing Information CC-BY-NC ### Citation Information ``` @article{hendersonkrass2022pileoflaw, title={Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset}, author={Henderson*, Peter and Krass*, Mark and Zheng, Lucia and Guha, Neel and Manning, Christopher and Jurafsky, Dan and Ho, Daniel E}, year={2022} } ```