README.md

---
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 concerning 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), derived from De-Arteaga et al. (2019). 
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 an L rather than an F", where F (foil) is another medical occupation, e.g., physician. 

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).

# 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', '.']
  "rationales": [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_rationales": [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]
  "annotations": [[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_annotations": [[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.*](https://aclanthology.org/2023.emnlp-main.427)

```
@inproceedings{eberle-etal-2023-rather,
    title = "Rather a Nurse than a Physician - Contrastive Explanations under Investigation",
    author = "Eberle, Oliver  and
      Chalkidis, Ilias  and
      Cabello, Laura  and
      Brandl, Stephanie",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.427",
}

```