Datasets:

uzw
/

canada_ssa_gender_neutral_first_names

Name stringlengths 2 24	Year int64 2.01k 2.02k	Total_sum int64 1 9.43k	Female_count int64 0 3.93k	Female_percentage float64 0 100	Gender stringclasses 3 values
Maliah	2,013	1	1	100	Female
Maxemilia	2,013	1	1	100	Female
Taliah	2,013	1	1	100	Female
Kenzi	2,013	5	5	100	Female
Seira	2,013	1	1	100	Female
Hartlyn	2,013	1	1	100	Female
Trae-Lynn	2,013	1	1	100	Female
Jaqueline	2,013	1	1	100	Female
Nakenya	2,013	1	1	100	Female
Teya	2,013	1	1	100	Female
Alisa	2,013	4	4	100	Female
Sahar	2,013	2	2	100	Female
Sukaina	2,013	1	1	100	Female
Lovelene	2,013	1	1	100	Female
Jaxynn	2,013	2	2	100	Female
Alysha	2,013	4	4	100	Female
Beth	2,013	1	1	100	Female
Raeanna	2,013	1	1	100	Female
Tamara	2,013	5	5	100	Female
Sahlara	2,013	1	1	100	Female
Carrie	2,013	2	2	100	Female
Helaina	2,013	1	1	100	Female
Kensi	2,013	2	2	100	Female
Aniya	2,013	6	6	100	Female
Curia	2,013	1	1	100	Female
Addie	2,013	3	3	100	Female
Aniston	2,013	1	1	100	Female
Neharika	2,013	1	1	100	Female
Evita	2,013	1	1	100	Female
Katherina-Jane	2,013	1	1	100	Female
Maub	2,013	1	1	100	Female
Kendal	2,013	3	3	100	Female
Madelen	2,013	1	1	100	Female
Sofia	2,013	101	101	100	Female
Ahlam	2,013	1	1	100	Female
Aidia	2,013	1	1	100	Female
Jillia	2,013	1	1	100	Female
Wareesha	2,013	1	1	100	Female
Mahlet	2,013	1	1	100	Female
Kasumi	2,013	1	1	100	Female
Adama	2,013	2	2	100	Female
Adeeb	2,013	1	1	100	Female
Kala	2,013	2	2	100	Female
Lootii	2,013	1	1	100	Female
Luise	2,013	1	1	100	Female
Omalkhayr	2,013	1	1	100	Female
Camilla	2,013	3	3	100	Female
Mahdia	2,013	1	1	100	Female
Kate	2,013	44	44	100	Female
Kaila	2,013	1	1	100	Female
Zofia	2,013	4	4	100	Female
Arianne	2,013	6	6	100	Female
Mòrag-Elizabeth	2,013	1	1	100	Female
Katarzyna	2,013	1	1	100	Female
Liezl	2,013	1	1	100	Female
Ariame	2,013	1	1	100	Female
Anéla	2,013	1	1	100	Female
Kimmy	2,013	1	1	100	Female
Lynessa	2,013	1	1	100	Female
Allyssandra	2,013	1	1	100	Female
Josanna	2,013	1	1	100	Female
Alyza	2,013	2	2	100	Female
Attley	2,013	1	1	100	Female
Shadai	2,013	1	1	100	Female
Giovanna	2,013	1	1	100	Female
Kyla	2,013	18	18	100	Female
Aleska	2,013	1	1	100	Female
Arella	2,013	2	2	100	Female
Ardynn	2,013	1	1	100	Female
Raquel	2,013	6	6	100	Female
Karisa	2,013	1	1	100	Female
Skarlett	2,013	1	1	100	Female
Coralie	2,013	1	1	100	Female
Elyn	2,013	3	3	100	Female
Cynthia	2,013	2	2	100	Female
Melodie	2,013	2	2	100	Female
Skyelar	2,013	1	1	100	Female
Milaya	2,013	1	1	100	Female
Murryla	2,013	1	1	100	Female
Eryss	2,013	1	1	100	Female
Annelia	2,013	1	1	100	Female
Shandae	2,013	1	1	100	Female
Seedra	2,013	1	1	100	Female
Savannah	2,013	53	53	100	Female
Lyvia	2,013	2	2	100	Female
Naveah	2,013	2	2	100	Female
Landra	2,013	1	1	100	Female
Emmel	2,013	1	1	100	Female
Jumanah	2,013	1	1	100	Female
Dhaya	2,013	1	1	100	Female
Nylah	2,013	10	10	100	Female
Umamah	2,013	1	1	100	Female
Suniti	2,013	1	1	100	Female
Aiysis	2,013	1	1	100	Female
Dava	2,013	1	1	100	Female
Aislee	2,013	1	1	100	Female
Kristyn	2,013	2	2	100	Female
Paisley	2,013	52	52	100	Female
Alexia	2,013	10	10	100	Female
Macayla	2,013	1	1	100	Female

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This is the official dataset for Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions

Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., "neutral", to study and address potential gender biases in Large Language Models (LLMs).

In this Canada SSA dataset, gender-neutral names were rare before 2000 (less than five first names per year) but increased in recent years (after 2010), we sampled 273 names per gender for each year from 2013 to 2020.

For Dynamic Gender Labe Dataset, please visit this page.

Dataset Statistics

We split the dataset into train/val/test sets. We keep all three genders (male/female/neutral) balanced across all three sets. Please see below and the paper for more details of our curated datasets:

Citation

Please cite the below paper if you intent to use our data for your research:

@inproceedings{you-etal-2024-beyond,
    title = "Beyond Binary Gender Labels: Revealing Gender Bias in {LLM}s through Gender-Neutral Name Predictions",
    author = "You, Zhiwen  and
      Lee, HaeJin  and
      Mishra, Shubhanshu  and
      Jeoung, Sullam  and
      Mishra, Apratim  and
      Kim, Jinseok  and
      Diesner, Jana",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Goldfarb-Tarrant, Seraphina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.gebnlp-1.16",
    doi = "10.18653/v1/2024.gebnlp-1.16",
    pages = "255--268",
}

Contact Information

If you have any questions, please email zhiweny2@illinois.edu.

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