Anyone involved with AI today, whether training, evaluating, or validating new systems, needs access to high-quality public search data. As search results become increasingly AI-driven, it is imperative to cite source data and ensure transparency and explainability in AI models.
Search data is particularly valuable because it captures how people actually phrase their questions, which topics they actively search for, and how information needs evolve over time. AI models built on public search data are more closely aligned with everyday language and user interests, especially when compared with systems trained on closed internal datasets or synthetic data. Access to public search results allows developers to make more precise improvements and document their methodology. This helps build trust in AI systems, as users can see how they were developed and how they generate their conclusions.
However, these benefits depend on securing access to the data in the first place.
The need for observable reality
Before we go any further, it’s important to describe how modern AI systems are now interacting with such information. Rather than drawing on existing, static datasets, they retrieve information from websites, databases, and application programming interfaces, or APIs, that allow them to have more informative data on hand. With such data, AI can aggregate and summarize content from sources as varied as news to social media, interact with other platforms to retrieve more data, and provide real-time data instead of regurgitating fixed knowledge.
AI is undergoing a profound shift. The early days of working from closed, pretrained models are long over, and the data is being pulled from many public information ecosystems.Â
While access to vast amounts of data provides unlimited opportunities for developers, there is a need for external reference points. AI models can be taught most anything, but they still provide results based on patterns, not on objective truth or quality. Reference points allow models to filterout hallucinations or errors, ensure that the results are relevant to the query, and measure progress objectively and over time.
Without such external reference points, AI systems risk becoming self-referential, and reinforcing their own mistakes while appearing to be confident in the results they return. This is not ideal.
Why open datasets matter
Open datasets, particularly those derived from public information, are increasingly useful because they can provide shared reference points that closed or proprietary datasets cannot.
As noted, AI models built on closed datasets are inadequate for a variety of reasons. They lack transparency, for one, and therefore are not reproducible. They also provide limited visibility into how people use systems day-to-day. While they might perform well in a closed environment, they will likely fail to meet users’ needs when tested.
Public search data makes all the difference here, as AI teams can use it to understand what information is available and to create and improve their models accordingly. They might analyze public search results to identify which sources are surfaced for common queries or which types of answers are most quickly made available. They can then compare those results with those produced by their own models. Public search data is also transparent and can be reproduced, unlike closed data.Â
That transparency also makes it easier for developers to comply with regulations and guidelines, and to make data available for auditing when necessary. Public datasets are therefore a shared reality against which AI systems can be evaluated and benchmarked.
Where do platforms like SerpApi fit in?
Companies exist that provide structured access to public search data, among them SerpApi, an Austin, Texas-based software-as-a-service company established in 2017. The company serves as a bridge, connecting the evolving landscape of search engines such as Google, Bing, and Baidu to developers who rely on such data to improve their models.
Since its founding, SerpApi has looked to replace old-fashioned, tedious, manual ways of getting structured public search data, such as relying on CAPTCHAs or constantly adjusting code to manage changes in search engine layouts, with its own suite of APIs. Users of its platform receive their data in a structured, JavaScript Object Notation (JSON) format, which makes it easy to dig through it for SEO monitoring, price comparison, or academic research.
Because of its flexibility and the richness of the data it can provide, SerpApi is seen in the industry as a technical enabler, a partner that can provide its clients with structured search engine results data from diverse platforms, at scale. Its standardized output is machine-readable and can be integrated into different dashboards and applications.Â
The data received back is consistent and benefits from the company’s in-house developed infrastructure, which was built to secure a high success rate and stable performance. And it’s constantly being retooled, as SerpApi’s dedicated team monitors and adapts to search engine algorithm and layout changes, so that data collection remains uninterrupted and consistent.
Search data as part of the AI stack
In conclusion, it’s important to see public search data as a foundational component of all modern AI systems. Having such data at hand provides the real-world, dynamic information that AI requires to understand what’s happening, how to best adapt to new conditions, and how to improve its own abilities.
It’s hard to think of better data than public search data, which is created by billions of people daily, on every topic imaginable, and shifts by the minute. That diversity will be key in improving AI models, and it cannot be replicated with internal datasets. Such data is relevant, timely, and also representative of how people ask questions, what words they use, and in what contexts. This avoids issues around bias introduced by outdated internal datasets to train new AI models.
But there are caveats. Any data used should be verifiable, meaning that it can be externally observed and confirmed by a third party. Such data is crucial for ensuring accuracy, reliability, and ethical compliance in AI systems. Garbage in, garbage out is the old maxim, but alongside it goes another that is truer to AI, quality in, quality out. Using verifiable data also allows you to provide transparency around your models, rather than just positioning them as black boxes. And don’t forget the roles that regulatory compliance and risk management play. Better to have verifiable data available that can help AI developers abide by the EU’s AI Act and GDPR.
Looking forward, there will only be a greater emphasis on maintaining trust through ensuring compliance and using verifiable data to increase trust and accountability in different models. After years of development in the private sector, regulators are also introducing new regulations that will further underscore the need for transparent data. But creating great AI systems isn’t just about checking regulatory boxes, it’s about making the best use of technology.
