SlowGuess/NeurIPS_2023_OCR huggingface dataset:
If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Not applicable
Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? The COCO-Counterfactuals dataset was collected automatically, as detailed in Section 3. Human evaluation of COCO-Counterfactuals involved paid professional annotators employed by Mindy Support (see Appendix B.2 for details).
Over what timeframe was the data collected? The data was generated and evaluated over the course of approximately three months.
Were any ethical review processes conducted (e.g., by an institutional review board)? No, institutional review was not required.
Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)? No, the dataset was generated automatically and was not collected directly from individuals.
Were the individuals in question notified about the data collection? Not applicable
Did the individuals in question consent to the collection and use of their data? Not applicable
If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? Not applicable
Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? No, not applicable
C.4 Preprocessing/cleaning/labeling
Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? Yes, we apply extensive filtering to various stages of our data generation pipeline in order to improve the quality of the dataset. See Section 3 for a complete description of these methods.
Was the "raw" data saved in addition to the preprocessed/cleaned/ labeled data (e.g., to support unanticipated future uses)? No. However, due to how our dataset is automatically constructed, raw data can be reproduced by running our code.
Is the software that was used to preprocess/clean/label the data available? Yes, we will make our code publicly available upon publication.
C.5 Uses
Has the dataset been used for any tasks already? Yes, we applied COCO-Counterfactuals to the task of model evaluation in Section 4 and to the task of training data augmentation in Section 5.
Is there a repository that links to any or all papers or systems that use the dataset? Our GitHub repository will contain links to papers and systems used by our data generation methodology. Additionally, this paper contains references to all such papers and systems that we utilized.
What (other) tasks could the dataset be used for? COCO-Counterfactuals is broadly applicable to tasks which require multimodal inputs consisting of images with paired text. One potential use case not explored during this study is large-scale pre-trianing of multimodal models, which could be improved through counterfactual data augmentation.
Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Due to the way in which COCO-Counterfactuals was generated automatically, it may contain errors, offensive material, or biases which are present in the models employed by our pipeline.
We used Stable Diffusion to collect image data, which has well-known limitations that should be considered when utilizing datasets which are derived from them. These limitations include unrealistic depictions of hands, palms, and other fine-grained objects (Samuel et al., 2023); failures to generate one or more of the subjects in a prompt and correctly bind attributes such as color (Chefer et al., 2023); difficulties with object counting and spatial relationship understanding (Cho et al., 2022); and challenges associated with the composition of concepts (Liu et al., 2022). While our experiments suggest that COCO-Counterfactuals is relatively robust to generation failures when used for training data augmentation, future applications of our methodology should consider the risks associated with these limitations relative to the intended use of the generated dataset.
Stable diffusion and other text-to-image diffusion models have been shown to exhibit biases associated with race and gender, including over-representation of masculinity and whiteness (Luccioni et al., 2023); racial and gender disparities in depictions of certain occupations (Bianchi et al., 2023); and preferences for certain genders or skin tones (Cho et al., 2022). Consequently, models trained on COCO-Counterfactuals may learn similar social biases as those expressed in synthetic images generated by Stable Diffusion. While some recent work has investigated approaches for mitigating biases in diffusion models, further investigation is needed into the de-biasing of datasets on which these models are trained in order to fully eliminate them (Schramowski et al., 2023).
Users of the dataset should carefully consider how these limitations may impact their potential use case.
Are there tasks for which the dataset should not be used? The dataset should not be used for a task if the limitations discussed above are unacceptable or potentially problematic for the intended use case.
C.6 Distribution
Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? Yes, the dataset will be made open source and publicly available.
How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? The dataset will be distributed via the Hugging Face Hub.
When will the dataset be distributed? The dataset will be made available publicly upon publication of this paper.
Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? The dataset will be distributed under the CC BY 4.0 license.
Have any third parties imposed IP-based or other restrictions on the data associated with the instances? No
Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? No
C.7 Maintenance
Who will be supporting/hosting/maintaining the dataset? The dataset will be hosted on the Hugging Face Hub. The authors of this paper will support and maintain the dataset via our public GitHub repository.
How can the owner/curator/manager of the dataset be contacted (e.g., email address)? The corresponding author can be contacted via the e-mail address listed on the first page of this paper. Alternatively, an issue can be raised on our GitHub repository.
Is there an erratum? No
Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? Although we do not anticipate the need to update this dataset in the future, we will respond to issues which are raised on our public GitHub repository for this project.
If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? Not applicable
Will older versions of the dataset continue to be supported/hosted/maintained? Yes. If the dataset is updated in the future, older versions will remain available.
If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? Yes, we make our dataset open source and welcome others to build on it. This can be done by making contributions to our GitHub repository and/or citing our dataset as appropriate when used in future work.