Web-Scraped AI Datasets and Privacy: Why CommonPool Deserves a Look

Artificial Intelligence (AI) has become a part of everyday life. It is visible in medical chatbots that guide patients and in generative tools that assist artists, writers, and developers. These systems appear advanced, yet they depend on a single essential resource: data.

Most of the data used to train AI systems comes from the public internet. Automated programs collect large volumes of text, images, and audio from online platforms. These collections form the foundation of well-known models such as GPT-4, Stable Diffusion, and many others. This vast collection, however, raises unresolved concerns about privacy, ownership, and informed consent.

The market for training datasets reflects the scale of this activity. As of now, the global value of AI datasets is estimated at 3.2 billion dollars. According to projections, it may grow to 16.3 billion dollars by 2034, with an annual growth rate of 20.5 percent. Behind these figures lies an important challenge. A significant portion of the collected material is obtained without explicit permission. It often contains personal data, copyrighted works, and other sensitive content that was never intended for machine learning systems.

In response to these issues, alternative approaches to data governance are being explored. One example is CommonPool, released in April 2023 as part of the DataComp benchmark. It is a large dataset of 12.8 billion image-text pairs designed for multimodal AI research. Unlike traditional scraping efforts, it applies filtering methods, emphasizes transparency, and includes community participation in its development. Although it remains subject to debate, CommonPool indicates an attempt to build more responsible and auditable practices for AI training data. Such initiatives highlight the need for ethical standards in the future of artificial intelligence.

The Role of Web-Scraped Data in Advancing Artificial Intelligence

Data is central to AI, with system performance closely linked to the amount and variety of information available for training. In recent years, Web scraping has become a standard method for assembling large datasets at scale. By collecting publicly accessible online content, researchers and developers have obtained vast and diverse data resources.

A popular example is Common Crawl, which by 2025 has stored petabytes of text collected through monthly crawls of more than 250 terabytes each. This dataset is widely used for training text-based AI models. Another example is LAION-5B, which contains about 5.85 billion image–text pairs. It has been important for applications such as Stable Diffusion, which can create realistic images from written prompts.

These datasets are valuable because they increase model accuracy, improve generalization through varied content, and allow smaller groups, including universities, to take part in AI development. The Stanford AI Index 2025 shows that most advanced models still rely on scraped data, with datasets growing rapidly in size. This demand has also driven heavy investment, reaching over 57 billion dollars in 2024 for data centres and computing power.

At the same time, web scraping is not free from challenges. It raises questions about privacy, ownership, and legal rights, since much of the collected content was not originally created for machine use. Court cases and policy discussions show that these challenges are becoming more urgent. The future of AI data collection will depend on finding a balance between progress and ethical responsibility.

The Privacy Problem with Scraped Data

Web scraping tools collect information without a clear separation between general content and sensitive details. Along with text and images, they often capture Personally Identifiable Information (PII) such as names, email addresses, and facial photographs.

An audit of the  CommonPool dataset in July 2025 revealed that even after filtering, 0.1% of the samples still contained identifiable faces, government IDs, and documents like résumés and passports. While the percentage appears small, at the scale of billions of records, it translates into hundreds of millions of affected individuals. Reviews and safety audits confirm that the presence of such material is not unusual, and its risks include identity theft, targeted harassment, and the unwanted exposure of private data.

Legal disputes are also increasing as concerns about data ownership and fair use move into the courts. Between 2023 and 2024, companies such as OpenAI and Stability AI faced lawsuits for using personal and copyrighted data without consent. In February 2025, a U.S. federal court ruled that training AI on unlicensed personal information counts as infringement. This decision has encouraged more class-action cases. Copyright is another major issue. Many scraped datasets contain books, articles, art, and code. Writers and artists argue that their work is being used without approval or payment. The ongoing New York Times v. OpenAI case questions whether AI systems reproduce protected content unlawfully. Visual artists have raised similar complaints, claiming that AI copies their individual style. In June 2025, one U.S. court supported an AI company under fair use, but experts say the rulings remain inconsistent and the legal framework is still unclear.

The lack of consent in AI training has weakened public trust. Many people discover that their blogs, creative work, or code are included in datasets without their knowledge. This has raised ethical concerns and calls for more transparency. In response, governments are moving toward stricter oversight through laws that promote fair development of AI models and careful use of data.

Why Scraped Datasets Are Hard to Replace

Even with concerns about privacy and consent, scraped datasets remain necessary for AI training. The reason is scale. Modern AI models require trillions of tokens from text, images, and other media. Building such datasets only through licensed or curated sources would cost hundreds of millions of dollars. This is not practical for most startups or universities.

High cost is not the only challenge with curated datasets. They often lack diversity and tend to focus on specific languages, regions, or communities. This narrow coverage makes AI models less balanced. In contrast, scraped data, despite being noisy and imperfect, captures a broader range of cultures, topics, and viewpoints. This diversity enables AI systems to perform better when applied to real-world use.

The risk, however, is that strict regulations could restrict access to scraped data. If this happens, smaller organizations may struggle to compete. Large companies with private or proprietary datasets, such as Google or Meta, would continue to advance. This imbalance could reduce competition and slow down open innovation in AI.

For now, scraped datasets are central to AI research. At the same time, projects like CommonPool are exploring ways to build extensive, ethically sourced collections. These efforts are necessary to keep the AI ecosystem more open, fair, and responsible.

CommonPool: Toward Responsible Large-Scale Data Engineering

CommonPool is one of the most technically ambitious efforts to build an open, large-scale multimodal dataset. With approximately 12.8 billion image–text pairs, it matches the scale of LAION-5B but integrates stronger data engineering and governance mechanisms. The key design goal was not only to maximize scale but also to align with principles of reproducibility, data provenance, and regulatory compliance.

The construction of the CommonPool dataset follows a structured three-stage pipeline. The first stage involves the extraction of raw samples from Common Crawl snapshots collected between 2014 and 2022. Both images and their associated text, such as captions or surrounding passages, are gathered. To evaluate semantic alignment, the maintainers apply CLIP-based similarity scoring, discarding pairs with weak correspondence between image and text embeddings. This early filtering step substantially reduces noise compared to naïve scraping pipelines.

In the second stage, the dataset undergoes large-scale deduplication. Perceptual hashing and MinHash techniques are used to identify and remove near-duplicate images, preventing redundancy from dominating model training. Additional filters are applied to exclude corrupted files, broken links, and low-resolution images. At this point, the pipeline also includes text normalization and automatic language identification, enabling the creation of domain-specific or language-specific subsets for targeted research.

The third stage focuses on safety and compliance. Automated face detection and blurring are applied, while child-related imagery and personal identifiers such as names, email addresses, and postal addresses are removed. The pipeline also attempts to detect copyrighted materials. Although no automated method can guarantee perfect filtering at Web scale, these safeguards represent a significant technical improvement compared with LAION-5B, where filtering was mainly limited to adult content and toxicity heuristics.

Beyond data processing, CommonPool introduces a governance model that distinguishes it from static dataset releases. It is maintained as a living dataset with versioned releases, structured metadata, and documented update cycles. Each sample includes licensing information where available, supporting compliance with copyright regulations. A takedown protocol allows individuals and institutions to request the removal of sensitive content, addressing concerns raised by the EU AI Act and related regulatory frameworks. Metadata such as source URLs and filtering scores improve transparency and reproducibility, enabling researchers to trace inclusion and exclusion decisions.

Benchmarking results from the DataComp initiative illustrate the technical effects of these design choices. When identical vision–language architectures were trained on LAION-5B and CommonPool, the latter produced models with more stable downstream performance, particularly on fine-grained retrieval and zero-shot classification tasks. These results suggest that CommonPool’s higher alignment quality compensates for some of the scale advantages of less filtered datasets. Nevertheless, independent audits in 2025 revealed residual risks: around 0.1% of the dataset still contained unblurred faces, sensitive personal documents, and medical records. This highlights the limits of even state-of-the-art automated filtering pipelines.

Overall, CommonPool represents a shift in dataset engineering from prioritizing raw scale to balancing scale, quality, and compliance. For researchers, it provides a reproducible and comparatively safer foundation for large-scale pretraining. For regulators, it demonstrates that privacy and accountability mechanisms can be embedded directly into dataset construction. In contrast with LAION, CommonPool illustrates how filtering pipelines, governance practices, and benchmarking frameworks can transform large-scale web data into a more technically robust and ethically responsible resource for multimodal AI.

Comparing CommonPool with Traditional Web-Scraped Datasets

Unlike earlier large-scale web-scraped datasets such as LAION-5B (5.85B samples), COYO-700M (700M samples), and WebLI (400M samples), CommonPool emphasizes structure, reproducibility, and governance. It retains metadata such as URLs and timestamps, which supports traceability and partial licensing checks. In addition, it applies CLIP-based semantic filtering to remove low-quality or weakly aligned image–text pairs, resulting in improved data quality.

By comparison, LAION-5B and COYO were assembled from Common Crawl with limited filtering and without detailed licensing documentation. These datasets frequently contain sensitive material, including medical records, identity documents, and unblurred faces. WebLI, used internally by OpenAI, also lacks transparency, as it was never released for external review or replication.

CommonPool seeks to address these issues by excluding PII and NSFW content, while acknowledging that full user consent remains unresolved. This makes it comparatively more reliable and ethically aligned than earlier alternatives.

The Bottom Line

The development of CommonPool reflects an important transition in how large-scale AI datasets are conceived and maintained. While earlier collections such as LAION-5B and COYO prioritized scale with limited oversight, CommonPool demonstrates that transparency, filtering, and governance can be integrated into dataset construction without undermining usability for research.

By retaining metadata, applying semantic alignment checks, and embedding privacy safeguards, it offers a more reproducible and accountable resource. At the same time, independent audits remind us that automated safeguards cannot entirely eliminate risks, highlighting the need for continued vigilance.