license:mit size_categories:10K<n<100K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
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license: mit
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# Dataset Card for Approximation of Political Neutrality Annotation Dataset

This dataset card accompanies the paper [Political Neturality in AI is Impossible- But Here is How to Approximate it](). This the dataset of annotatd generations of political questions. 

## Dataset Details

### Dataset Description

It includes the input propmts, output generations, as well as the annotated approxiamtions of political neutrality techniques. 


- **Curated by:** Jillian Fisher
- **Language(s) (NLP):** [English]

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/jfisher52/Approximation_Political_Neutrality
- **Paper:** [More Information Needed]


## Dataset Structure

A description of the columns of the file are as follows:

ID: Unique ID for each input (task_input-num_format-num_model_eval-num)

Task: question type

Input Number: Number of unique question within a specific task

Format: Question format for a specifric unique question

Input: Prompt for model genereation

\<Model\>_Generation = Generation from \<Model\>

\<Model\>_Annotation = Final annotation from GPT-4o (1 = "Refusal", 2 = "Avoidance", 3 = "Reasonable Pluralism", 4 = "Local Transparency", 5 = "No Approximated Used", 6 = "Biased Answer")

## Dataset Creation

### Source Data

See full data description and annotation procedure in paper appendix. 

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]


## Dataset Card Contact

jrfish@uw.edu