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---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: medical-bios
size_categories:
- 1K<n<10K
---
# Dataset Description
The dataset comprises English biographies labeled with occupations and binary genders.
This is an occupation classification task, where bias with respect to gender can be studied.
It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician).
We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive.
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.
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.
# Dataset Structure
We provide the `standard` version of the dataset, where examples look as follows.
```json
{
"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. ... ",
"label": 3,
}
```
and the newly curated subset of examples including human rationales, dubbed `rationales', where examples look as follows.
```json
{
"text": "'She is currently practising at Dr Ravindra Ratolikar Dental Clinic in Narayanguda, Hyderabad.",
"label": 3,
"foil": 2,
"words": ['She', 'is', 'currently', 'practising', 'at', 'Dr', 'Ravindra', 'Ratolikar', 'Dental', 'Clinic', 'in', 'Narayanguda', ',', 'Hyderabad', '.']
"rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"contrastive_rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
# Use
To load the `standard` version of the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("coastalcph/medical-bios", "standard")
```
To load the newly curated subset of examples with human rationales:
```python
from datasets import load_dataset
dataset = load_dataset("coastalcph/medical-bios", "rationales")
```
# Citation
[*Oliver Eberle\*, Ilias Chalkidis\*, Laura Cabello, Stephanie Brandl. Rather a Nurse than a Physician - Contrastive Explanations under Investigation. 2023. In the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore.*](xxx)
```
@inproceedings{eberle-etal-2023-contrast-bios,
author={Oliver Eberle, Ilias Chalkidis, Laura Cabello, Stephanie Brandl},
title={Rather a Nurse than a Physician - Contrastive Explanations under Investigation},
booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
address={Singapore, Singapore}
}
```
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