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@@ -9,16 +9,19 @@ tags:
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  pretty_name: medical-bios
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  size_categories:
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  - 1K<n<10K
 
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  ---
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  # Dataset Description
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  The dataset comprises English biographies labeled with occupations and binary genders.
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- This is an occupation classification task, where bias with respect to gender can be studied.
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- It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician).
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  We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive.
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  In the former, the annotators were asked to find the rationale for the question: "Why is the person in the following short bio described as a L?", where L is the gold label occupation, e.g., nurse.
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- In the latter, the question was "Why is the person in the following short bio described as a L rather than a F", where F (foil) is another medical occupation, e.g., physician.
 
 
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  # Dataset Structure
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@@ -26,7 +29,7 @@ We provide the `standard` version of the dataset, where examples look as follows
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  ```json
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  {
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- "text": "He has been a practicing Dentist for 20 years. He has done BDS . He is currently associated with Sree Sai Dental Clinic in Sowkhya Ayurveda Speciality Clinic, Chennai. ... ",
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  "label": 3,
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  }
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  ```
 
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  pretty_name: medical-bios
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  size_categories:
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  - 1K<n<10K
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+
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  ---
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  # Dataset Description
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  The dataset comprises English biographies labeled with occupations and binary genders.
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+ This is an occupation classification task, where bias concerning gender can be studied.
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+ It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician), derived from De-Arteaga et al. (2019).
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  We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive.
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  In the former, the annotators were asked to find the rationale for the question: "Why is the person in the following short bio described as a L?", where L is the gold label occupation, e.g., nurse.
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+ In the latter, the question was "Why is the person in the following short bio described as an L rather than an F", where F (foil) is another medical occupation, e.g., physician.
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+
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+ You can read more details on the dataset and the annotation process in the paper [Eberle et al. (2023)](https://arxiv.org/abs/2310.11906).
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  # Dataset Structure
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  ```json
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  {
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+ "text": "He has been a practicing Dentist for 20 years. He has done BDS. He is currently associated with Sree Sai Dental Clinic in Sowkhya Ayurveda Speciality Clinic, Chennai. ... ",
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  "label": 3,
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  }
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  ```