# Dataset Card for CivilComments WILDS

## Dataset Description

- **Homepage:**  https://wilds.stanford.edu/datasets/#civilcomments
- **Repository:** 
- **Paper:** https://arxiv.org/abs/2012.07421 | https://arxiv.org/abs/1903.04561
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

![](https://wilds.stanford.edu/assets/images/dataset_figures/civilcomments_dataset.jpg)

Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics ([Park et al., 2018](https://arxiv.org/abs/1808.07231); [Dixon et al., 2018](https://research.google/pubs/pub46743/)). These types of spurious correlations can significantly degrade model performance on particular subpopulations ([Sagawa et al.,2020](https://arxiv.org/abs/1911.08731)).

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

English

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

This dataset is in the public domain and is distributed under [CC0](https://creativecommons.org/publicdomain/zero/1.0/).

### Citation Information

@inproceedings{wilds2021,
    title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
    author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
    Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
    Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
    Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
    and Percy Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
}

@inproceedings{borkan2019nuanced,
    title={Nuanced metrics for measuring unintended bias with real data for text classification},
    author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
    booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
    pages={491--500},
    year={2019}
}

@article{DBLP:journals/corr/abs-2211-09110,
  author       = {Percy Liang and
                  Rishi Bommasani and
                  Tony Lee and
                  Dimitris Tsipras and
                  Dilara Soylu and
                  Michihiro Yasunaga and
                  Yian Zhang and
                  Deepak Narayanan and
                  Yuhuai Wu and
                  Ananya Kumar and
                  Benjamin Newman and
                  Binhang Yuan and
                  Bobby Yan and
                  Ce Zhang and
                  Christian Cosgrove and
                  Christopher D. Manning and
                  Christopher R{\'{e}} and
                  Diana Acosta{-}Navas and
                  Drew A. Hudson and
                  Eric Zelikman and
                  Esin Durmus and
                  Faisal Ladhak and
                  Frieda Rong and
                  Hongyu Ren and
                  Huaxiu Yao and
                  Jue Wang and
                  Keshav Santhanam and
                  Laurel J. Orr and
                  Lucia Zheng and
                  Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
                  Mirac Suzgun and
                  Nathan Kim and
                  Neel Guha and
                  Niladri S. Chatterji and
                  Omar Khattab and
                  Peter Henderson and
                  Qian Huang and
                  Ryan Chi and
                  Sang Michael Xie and
                  Shibani Santurkar and
                  Surya Ganguli and
                  Tatsunori Hashimoto and
                  Thomas Icard and
                  Tianyi Zhang and
                  Vishrav Chaudhary and
                  William Wang and
                  Xuechen Li and
                  Yifan Mai and
                  Yuhui Zhang and
                  Yuta Koreeda},
  title        = {Holistic Evaluation of Language Models},
  journal      = {CoRR},
  volume       = {abs/2211.09110},
  year         = {2022},
  url          = {https://doi.org/10.48550/arXiv.2211.09110},
  doi          = {10.48550/arXiv.2211.09110},
  eprinttype    = {arXiv},
  eprint       = {2211.09110},
  timestamp    = {Wed, 23 Nov 2022 18:03:56 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2211-09110.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

### Contributions

[More Information Needed]
task_categories:text-classification size_categories:100K<n<1M language:en license:cc0-1.0 toxicity arxiv:2012.07421 arxiv:1903.04561 arxiv:1808.07231 arxiv:1911.08731 arxiv:2211.09110 region:us
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "pretty_name": "CivilComments WILDS", "tags": ["toxicity"]}
TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc0-1.0 #toxicity #arxiv-2012.07421 #arxiv-1903.04561 #arxiv-1808.07231 #arxiv-1911.08731 #arxiv-2211.09110 #region-us 

# Dataset Card for CivilComments WILDS

## Dataset Description

- Homepage:  URL
- Repository: 
- Paper: URL | URL
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

![](URL

Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics (Park et al., 2018; Dixon et al., 2018). These types of spurious correlations can significantly degrade model performance on particular subpopulations (Sagawa et al.,2020).

### Supported Tasks and Leaderboards



### Languages

English

## Dataset Structure

### Data Instances



### Data Fields



### Data Splits



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information

This dataset is in the public domain and is distributed under CC0.



@inproceedings{wilds2021,
    title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
    author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
    Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
    Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
    Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
    and Percy Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
}

@inproceedings{borkan2019nuanced,
    title={Nuanced metrics for measuring unintended bias with real data for text classification},
    author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
    booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
    pages={491--500},
    year={2019}
}

@article{DBLP:journals/corr/abs-2211-09110,
  author       = {Percy Liang and
                  Rishi Bommasani and
                  Tony Lee and
                  Dimitris Tsipras and
                  Dilara Soylu and
                  Michihiro Yasunaga and
                  Yian Zhang and
                  Deepak Narayanan and
                  Yuhuai Wu and
                  Ananya Kumar and
                  Benjamin Newman and
                  Binhang Yuan and
                  Bobby Yan and
                  Ce Zhang and
                  Christian Cosgrove and
                  Christopher D. Manning and
                  Christopher R{\'{e}} and
                  Diana Acosta{-}Navas and
                  Drew A. Hudson and
                  Eric Zelikman and
                  Esin Durmus and
                  Faisal Ladhak and
                  Frieda Rong and
                  Hongyu Ren and
                  Huaxiu Yao and
                  Jue Wang and
                  Keshav Santhanam and
                  Laurel J. Orr and
                  Lucia Zheng and
                  Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
                  Mirac Suzgun and
                  Nathan Kim and
                  Neel Guha and
                  Niladri S. Chatterji and
                  Omar Khattab and
                  Peter Henderson and
                  Qian Huang and
                  Ryan Chi and
                  Sang Michael Xie and
                  Shibani Santurkar and
                  Surya Ganguli and
                  Tatsunori Hashimoto and
                  Thomas Icard and
                  Tianyi Zhang and
                  Vishrav Chaudhary and
                  William Wang and
                  Xuechen Li and
                  Yifan Mai and
                  Yuhui Zhang and
                  Yuta Koreeda},
  title        = {Holistic Evaluation of Language Models},
  journal      = {CoRR},
  volume       = {abs/2211.09110},
  year         = {2022},
  url          = {URL
  doi          = {10.48550/arXiv.2211.09110},
  eprinttype    = {arXiv},
  eprint       = {2211.09110},
  timestamp    = {Wed, 23 Nov 2022 18:03:56 +0100},
  biburl       = {URL
  bibsource    = {dblp computer science bibliography, URL}
}

### Contributions


# Dataset Card for CivilComments WILDS ## Dataset Description

- Homepage:  URL
- Repository: 
- Paper: URL | URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

![](URL

Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics (Park et al., 2018; Dixon et al., 2018). These types of spurious correlations can significantly degrade model performance on particular subpopulations (Sagawa et al.,2020). ### Supported Tasks and Leaderboards ### Languages

English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

This dataset is in the public domain and is distributed under CC0.



@inproceedings{wilds2021,
    title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
    author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
    Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
    Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
    Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
    and Percy Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
}

@inproceedings{borkan2019nuanced,
    title={Nuanced metrics for measuring unintended bias with real data for text classification},
    author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
    booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
    pages={491--500},
    year={2019}
}

@article{DBLP:journals/corr/abs-2211-09110,
  author       = {Percy Liang and
                  Rishi Bommasani and
                  Tony Lee and
                  Dimitris Tsipras and
                  Dilara Soylu and
                  Michihiro Yasunaga and
                  Yian Zhang and
                  Deepak Narayanan and
                  Yuhuai Wu and
                  Ananya Kumar and
                  Benjamin Newman and
                  Binhang Yuan and
                  Bobby Yan and
                  Ce Zhang and
                  Christian Cosgrove and
                  Christopher D. Manning and
                  Christopher R{\'{e}} and
                  Diana Acosta{-}Navas and
                  Drew A. Hudson and
                  Eric Zelikman and
                  Esin Durmus and
                  Faisal Ladhak and
                  Frieda Rong and
                  Hongyu Ren and
                  Huaxiu Yao and
                  Jue Wang and
                  Keshav Santhanam and
                  Laurel J. Orr and
                  Lucia Zheng and
                  Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
                  Mirac Suzgun and
                  Nathan Kim and
                  Neel Guha and
                  Niladri S. Chatterji and
                  Omar Khattab and
                  Peter Henderson and
                  Qian Huang and
                  Ryan Chi and
                  Sang Michael Xie and
                  Shibani Santurkar and
                  Surya Ganguli and
                  Tatsunori Hashimoto and
                  Thomas Icard and
                  Tianyi Zhang and
                  Vishrav Chaudhary and
                  William Wang and
                  Xuechen Li and
                  Yifan Mai and
                  Yuhui Zhang and
                  Yuta Koreeda},
  title        = {Holistic Evaluation of Language Models},
  journal      = {CoRR},
  volume       = {abs/2211.09110},
  year         = {2022},
  url          = {URL
  doi          = {10.48550/arXiv.2211.09110},
  eprinttype    = {arXiv},
  eprint       = {2211.09110},
  timestamp    = {Wed, 23 Nov 2022 18:03:56 +0100},
  biburl       = {URL
  bibsource    = {dblp computer science bibliography, URL}
} ### Contributions
TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc0-1.0 #toxicity #arxiv-2012.07421 #arxiv-1903.04561 #arxiv-1808.07231 #arxiv-1911.08731 #arxiv-2211.09110 #region-us 
 # Dataset Card for CivilComments WILDS ## Dataset Description

- Homepage:  URL
- Repository: 
- Paper: URL | URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

![](URL

Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics (Park et al., 2018; Dixon et al., 2018). These types of spurious correlations can significantly degrade model performance on particular subpopulations (Sagawa et al.,2020). ### Supported Tasks and Leaderboards ### Languages

English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

This dataset is in the public domain and is distributed under CC0.



@inproceedings{wilds2021,
    title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
    author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
    Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
    Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
    Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
    and Percy Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
}

@inproceedings{borkan2019nuanced,
    title={Nuanced metrics for measuring unintended bias with real data for text classification},
    author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
    booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
    pages={491--500},
    year={2019}
}

@article{DBLP:journals/corr/abs-2211-09110,
  author       = {Percy Liang and
                  Rishi Bommasani and
                  Tony Lee and
                  Dimitris Tsipras and
                  Dilara Soylu and
                  Michihiro Yasunaga and
                  Yian Zhang and
                  Deepak Narayanan and
                  Yuhuai Wu and
                  Ananya Kumar and
                  Benjamin Newman and
                  Binhang Yuan and
                  Bobby Yan and
                  Ce Zhang and
                  Christian Cosgrove and
                  Christopher D. Manning and
                  Christopher R{\'{e}} and
                  Diana Acosta{-}Navas and
                  Drew A. Hudson and
                  Eric Zelikman and
                  Esin Durmus and
                  Faisal Ladhak and
                  Frieda Rong and
                  Hongyu Ren and
                  Huaxiu Yao and
                  Jue Wang and
                  Keshav Santhanam and
                  Laurel J. Orr and
                  Lucia Zheng and
                  Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
                  Mirac Suzgun and
                  Nathan Kim and
                  Neel Guha and
                  Niladri S. Chatterji and
                  Omar Khattab and
                  Peter Henderson and
                  Qian Huang and
                  Ryan Chi and
                  Sang Michael Xie and
                  Shibani Santurkar and
                  Surya Ganguli and
                  Tatsunori Hashimoto and
                  Thomas Icard and
                  Tianyi Zhang and
                  Vishrav Chaudhary and
                  William Wang and
                  Xuechen Li and
                  Yifan Mai and
                  Yuhui Zhang and
                  Yuta Koreeda},
  title        = {Holistic Evaluation of Language Models},
  journal      = {CoRR},
  volume       = {abs/2211.09110},
  year         = {2022},
  url          = {URL
  doi          = {10.48550/arXiv.2211.09110},
  eprinttype    = {arXiv},
  eprint       = {2211.09110},
  timestamp    = {Wed, 23 Nov 2022 18:03:56 +0100},
  biburl       = {URL
  bibsource    = {dblp computer science bibliography, URL}
} ### Contributions
passage: TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc0-1.0 #toxicity #arxiv-2012.07421 #arxiv-1903.04561 #arxiv-1808.07231 #arxiv-1911.08731 #arxiv-2211.09110 #region-us 
# Dataset Card for CivilComments WILDS## Dataset Description

- Homepage:  URL
- Repository: 
- Paper: URL | URL
- Leaderboard: 
- Point of Contact:### Dataset Summary

![](URL

Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics (Park et al., 2018; Dixon et al., 2018). These types of spurious correlations can significantly degrade model performance on particular subpopulations (Sagawa et al.,2020).### Supported Tasks and Leaderboards### Languages

English## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators