license:mit size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
dataset_info:
  features:
  - name: baseline
    dtype: string
  - name: less_pleasant
    dtype: string
  - name: txt
    dtype: string
  - name: hard_label
    dtype: int64
  splits:
  - name: train
    num_bytes: 4922906
    num_examples: 13737
  - name: validation
    num_bytes: 1653062
    num_examples: 4807
  - name: test
    num_bytes: 1507698
    num_examples: 4271
  download_size: 4180637
  dataset_size: 8083666
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---



## Description
This dataset was created as part of the "Research Engineers Club" initiative organized by Apollo Research. The project aims to reproduce some papers around safety and alignement AI.

The dataset is based on the utilitarianism section of the ETHICS dataset and has been reformatted into a Q/A style. The original baseline and less pleasant options have been shuffled for each example to ensure variety in the presented questions.
This dataset present the original dataset in a question-answer format. Each example provides two possible scenarios (one more pleasant and one less pleasant), with the task of identifying which is more pleasant.
## Dataset Features
- **baseline**: The more pleasant scenario.
- **less_pleasant**: The less pleasant scenario.
- **txt**: The formatted question, presenting two options and asking which is more pleasant.
- **hard_label**: A binary label indicating whether the more pleasant option was presented first or second.

## Dataset Structure
The dataset is divided into three splits: `train`, `validation`, and `test`.

- **Train**: 13,737 examples
- **Validation**: 4,807 examples
- **Test**: 4,271 examples

Each split contains formatted questions based on utilitarian ethical scenarios.

## License
This dataset is released under the MIT license.

## Usage
This dataset is suitable for tasks such as:
- Ethical decision-making models
- Question-answering tasks
- Research in moral philosophy and AI

You can load the dataset using the `datasets` library as follows:

```python
from datasets import load_dataset

dataset = load_dataset("aminert/w2c_rec_ethics_utilitarianism_dataset")
```

---

