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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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viever: true |
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# Bias in Bios |
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Bias in Bios was created by (De-Artega et al., 2019) and published under the MIT license (https://github.com/microsoft/biosbias). The dataset is used to investigate bias in NLP models. It consists of textual biographies used to predict professional occupations, the sensitive attribute is the gender (binary). |
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The version shared here is the version proposed by (Ravgofel et al., 2020) which slightly smaller due to the unavailability of 5,557 biographies. |
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The dataset is divided between train (257,000 samples), test (99,000 samples) and dev (40,000 samples) sets. |
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Below are presented the classifiaction and sensitive attribtues labels and their proportion. Distributions are similar through the three sets. |
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#### Classification labels |
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| Profession | Numerical label | Proportion (%)| | Profession | Numerical label | Proportion (%)| |
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accountant | 0 | 1.42 | | nurse | 13 | 4.78 |
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architect | 1 | 2.55 | | painter | 14 | 1.95 |
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attorney | 2 | 8.22 | | paralegal | 15 | 0.45 |
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chiropractor | 3 | 0.67 | | pastor | 16 | 0.64 |
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comedian | 4 | 0.71 | | personal_trainer | 17 | 0.36 |
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composer | 5 | 1.41 | | photographer | 18 | 6.13 |
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dentist | 6 | 3.68 | | physician | 19 | 10.35 |
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dietitian | 7 | 1.0 | | poet | 20 | 1.77 |
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dj | 8 | 0.38 | | professor | 21 | 29.8 |
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filmmaker | 9 | 1.77 | | psychologist | 22 | 4.64 |
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interior_designer | 10 | 0.37 | | rapper | 23 | 0.35 |
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journalist | 11 | 5.03 | | software_engineer | 24 | 1.74 |
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model | 12 | 1.89 | | surgeon | 25 | 3.43 |
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nurse | 13 | 4.78 | | teacher | 26 | 4.09 |
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painter | 14 | 1.95 | | yoga_teacher | 27 | 0.42 |
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#### Sensitive attributes |
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| Gender | Numerical label | Proportion (%)| |
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Male | 0 | 53.9 | |
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Female | 1 | 46.1 |
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(De-Artega et al., 2019) Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019. Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Association for Computing Machinery, New York, NY, USA, 120–128. https://doi.org/10.1145/3287560.3287572 |
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(Ravgofel et al., 2020) Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7237–7256, Online. Association for Computational Linguistics. |
