9 Ways To See A Dataset: Datasets as sociotechnical artifacts — The case of 'Colossal Cleaned Common Crawl' (C4)
Will Orr
Illicit Nature: Birds, plants, and critters that would be erased from the C4 dataset


The capabilities of artificial intelligence (AI) systems are bound by the datasets used to train them. Datasets encapsulate the concepts, representations, and notions of truth upon which AI relies. Training datasets carry inherent social and political perspectives into AI systems, such as how people, objects, and ideas are represented, influencing how AI systems allocate resources and opportunities.

Scholars have turned to dataset auditing to uncover the social and political nature of datasets by excavating the assumptions underpinning training datasets. While auditing datasets can uncover the representational and allocative harms embedded within datasets, they fail to reveal why datasets embody specific political perspectives or offer insights into how the practices of dataset creation can be improved. Rather, viewing datasets through the lens of their creators shows them to be less stable and far more complex and arbitrary than commonly understood, resulting from trade-offs, compromises, and constraints present at their formation. In short, datasets are products of their sociotechnical contexts.

The case of the Colossal Cleaned Common Crawl (C4) dataset underscores the importance of uncovering the sociotechnical dynamics that contribute to the formation of datasets. While audits of C4 reveal the presence of harmful content, misinformation, and the exclusion of content about marginalized individuals, a creator of C4 points to the organizational, resource, and personal factors that shaped the dataset's final form. Understanding the conditions of dataset creation is crucial for improving the practices of dataset creation and preventing the creation of datasets that can produce and perpetuate societal harm in the future.

What is C4?

The Colossal Cleaned Common Crawl dataset, or C4, is a large-scale text corpus developed by a team of Google engineers. It was created by taking a single month's scrape of the Common Crawl corpus and using filtering heuristics to remove duplicate, placeholder, nonsensical, and non-English language content. Offensive content was also filtered by erasing any web page that contained any of the over 400 words found on the "List of Dirty, Naughty, Obscene, and Otherwise Bad Words", an open-source project originally developed by Shutterstock engineers to sanitize the results of its autocomplete search suggestions. The list has since been widely used and is available in 28 languages including Esperanto and Klingon.

These methods produce a dataset of approximately 750 GB of "reasonably clean and natural English text" that can be used for pre-training large language models. Google uses C4 to train its open-source language model, T5, which it uses in its proprietary text-to-image generative model, Imagen. More recently, C4 has been used to train Google's large language model LaMDA, making up 12.5% of its training corpus.

Understanding C4 through audits

Given the profound impact that C4 can have on large models, researchers have turned to auditing the dataset to understand the kinds of data contained within it. These audits have revealed the limitation of the dataset and the flaws of the filtering pipeline to remove harmful and inappropriate content.

For instance, the Washington Post uncovered a considerable quantity of pornographic and offensive content, including over 72,000 instances of the word "swastika", one of the words present on the block list. It also identified content sourced from sites associated with anti-trans perspectives, white supremacists, far-right conspiracy theories such as Q-Anon, and misinformation. Another study found that C4 disproportionately excludes content about minoritized individuals, such as non-sexual and non-offensive content about LGBT+ people, and content associated with Black and Hispanic authors. Models trained upon this corpus will thus suppress the existence of marginalized people while further amplifying abusive perspectives toward them. These audits underscore that data cleaning is not a value-neutral practice, and the decisions made during its creation mold the political implications of a dataset and the voices represented within it.

Datasets as sociotechnical products

However, these audits provide limited insights into the obstacles faced by dataset creators in the development of datasets for machine learning applications. Rather than seeking to expose the harmful, abusive, and copyright-protected content present within datasets, qualitative research with dataset creators seeks to make sense of the complications present within datasets. In July 2022, I spoke with a creator of C4 about their dataset. The creator acknowledged that C4 is far from perfect. They saw it not as a "proper dataset" constructed with human labor or considered curation. Rather, it was "haphazardly stitched together" as a compromise of various organizational, personal, and resource constraints. These trade-offs were seen as necessary to meet the project's goals within the creators' operational constraints.

Organizational constraints

A primary objective of the project was to create a publicly accessible dataset suitable for pre-training language models, in contrast to the private, proprietary datasets typically used for training large proprietary models. However, the C4 creators needed approval from Google's legal team, who acted as the final arbiter of what was permitted to be in the dataset. The restrictions imposed by the legal team materially impacted the type of data that was available to be used within the dataset, as well as the final form of the dataset.

In order to release the dataset publicly, they were only able to use publicly available resources. This prevented the team from using Google's own private web scrape (the collection of text and metadata about accessible sites on the web), a valuable asset used by Google to produce search engine results and train proprietary models such as Google's chatbot, Bard. Instead, C4's creators were required to rely on flawed-yet-publicly-available resources, such as Common Crawl. While Google may have more control over limiting inappropriate content from its web scrape, Common Crawl is known to include pornographic and abusive content, hate speech, and racial and sexual biases, making filtering the dataset essential before it can be used for pre-training.

Google's legal team also prohibited the C4 creators from releasing the dataset itself. Instead, the creators released code that could be used to reproduce the C4 dataset, which has since been recreated and is hosted publicly.

Personal constraints

Beyond Google's organizational constraints, the engineering team also had its own reservations that shaped the form of the final dataset. Primarily, C4 was created to train Google's open-source language model, the Text-To-Text Transfer Transformer, or T5. The creator that I interviewed explained that of the team of nine engineers developing T5, no one was "the dataset person" responsible for ensuring that C4 was of high quality and suitable for training a language model.

The absence of a dedicated dataset curator had implications for the cleaning process. The team was reluctant to impose their own beliefs about what constitutes "good web content" to properly curate Common Crawl. They acknowledged the immense responsibility of creating a block list or filtering heuristic that defines what harmful web content is, as well as content considered permissible and safe for training. However, no one on the team felt capable of coordinating or leading this effort. Nor would Google permit them to release their own exclusion list.

To alleviate this burden, the team opted to use a publicly available block list. Doing so allowed them to defer accountability for defining what was considered safe and appropriate to the creators of this list. They also perceived that using an existing block list provided a sense of security for their team, shielding their opinions and subjectivities from becoming embedded in the dataset infrastructure. This was a strategic attempt to distance themselves from the potential biases and controversies associated with curating web content. While the creator that I spoke to recognized that the filtering pipeline was itself flawed, it was considered "the best bad option" available. The C4 team's decision to not alter or improve the block list mirrors assumptions that model creators make about C4 being "good enough" for their use.

Resource constraints

Finally, creators also faced resource constraints such as time pressures and limited human labor. The C4 creator I interviewed emphasized that manual data curation work is crucial for creating high-quality datasets. However, the engineering team lacked the necessary time and resources to undertake this complex and nuanced task themselves. They acknowledged that comprehensively curating the dataset would be a long-term project with multiple stakeholders, which was beyond the capacity of their engineering team.

The C4 creator explained that dataset creation is "all a matter of cost-benefit". Considering time pressures and limited human labor, they made the strategic decision to implement a pipeline that would satisfy their operational constraints quickly. This necessitated utilizing publicly available resources, which, although flawed, offered a practical solution to their constraints.

A faulty pipeline

Operational constraints often require dataset creators to make challenging decisions and compromises. The case of C4 highlights how the sociotechnical context of the dataset's creation shaped its final form. Though there were some "general mixed feelings" about using a block list, their constraints led to the implementation of a "bone-headed filtering" pipeline that inadvertently removed content about, and produced by, marginalized people. The exclusion of marginalized groups from the final dataset was "somewhat expected but not intended" by creators due to their flawed filtering pipeline. This case highlights the trade-offs and compromises that are made within the construction of datasets that are erased from the final published dataset. Despite its limitations, the dataset was deemed "clean enough" for pre-training a large language model to perform well on standard Natural Language Processing (NLP) benchmarks, which was a primary objective of the project.

While researchers may look to auditing datasets to understand their social and political implications, auditing datasets in their final form solely based on their contents overlooks the crucial sociotechnical conditions that shape them. Dataset creators provide a valuable entry point into interrogating datasets as contingent and unstable artifacts in the making. These insights are crucial for uncovering the complex trade-offs and compromises made in the construction of datasets that ultimately shape their social and political implications. Understanding these conditions of dataset creation is imperative to identify deficiencies and drive improvements in responsible dataset creation practices.