Despite the rise of generative AI tools like ChatGPT and Gemini, human-generated data remains crucial for AI developers. Companies like OpenAI invest heavily in obtaining human-generated data to train their large language models (LLMs).1 Whether through data collection services or in-house efforts, AI developers require a steady stream of human-generated data.
Here, we offer a guide to human-generated data, why it’s still important, and how companies can access it.
Top 3 methods of accessing human-generated data
1. Crowdsourcing
Crowdsourcing is an effective way to avoid the previously mentioned challenges, specifically the time-consuming and cost-related ones. Through crowdsourcing, a large group of people generates data and shares it through an online platform (Which the company needs to develop or purchase). This way, a large amount of data can be generated in a shorter period of time.
The crowd generates the data using its own equipment, eliminating the extra costs of purchasing equipment or hiring contributors.
Recommendations
This method is suitable for projects with budget and time constraints and requires diverse human-generated content. It would not be suitable for projects of a secretive nature, such as government projects.
You can work with a crowdsourcing service if you do not wish to go through the hassle of developing and managing a crowdsourcing platform. Some service providers also offer data protection for projects of a secretive nature, so it is important to consider that while selecting a vendor.
2. In-house data collection
Human data can also be generated in-house if the company is willing to spare personnel, time, and money. In this method, a team is dedicated to the process, which recruits the contributors, purchases the necessary equipment, and processes the data after collection. This method can allow the company to generate highly personalized datasets in a private setting.
Recommendations
This method is best suited for projects of a confidential nature. Since the data does not leave the company servers, it stays confidential. For instance, the data must be collected in-house to train machine learning models for a government project. This method is unsuitable for collecting large-scale datasets created by humans since it can take the budget and timeline of the project to unreasonable heights.
You can check our data-driven list of data collection/harvesting services to find the best option that suits your business/project needs.
3. Pre-packaged/public datasets
There are also prepackaged datasets that are generated by humans and can be accessed for free or purchased for a price. Third-party firms generate and sell such prepackaged datasets for different applications, such as machine learning development, and update them regularly. Public datasets are generated by the general public to promote the growth and development of AI solutions.
For instance, a public, free-to-download dataset can be made available to support the development of the facial recognition industry.
Recommendations
Public datasets can sometimes have quality issues since they are generated by the general public and do not undergo rigorous quality checks. Prepackaged datasets have better quality than public datasets, but lack uniqueness. Therefore, they cannot be used for projects that have unique data requirements.
Such datasets are good for projects with a limited budget and time, and that do not require high levels of quality and personalization.
| Method | Pros | Cons | Best For |
|---|---|---|---|
| Crowdsourcing | Fast scaling, low equipment cost, diverse inputs | Quality control challenges, platform fees | Projects needing diversity & speed |
| In‑House Collection | Full confidentiality, custom protocols | High fixed costs; long timelines; limited to organizational capacity | Confidential or regulated‑sector work |
| Pre‑packaged/Public Datasets | Immediate availability, often well‑documented | May lack specificity; varying quality; licensing constraints | Exploratory projects, budget constraints |
What is human-generated data?
Human-generated data is data that is created by people through human action, as opposed to machine learning or other artificial means. This can include anything from text data to social media posts to pictures and videos. Even though machine-generated data and technologies like generative AI are becoming more popular. Human-generated data remains an important source of information for businesses and tech developers.
Why is it still important?
Human-generated data will become a more critical asset for businesses as technology improves. This section highlights some benefits of human-generated data that are still relevant.
1. Caters to exclusive requirements
There are some projects or applications where only human-generated data can be used. For instance, if a facial recognition or an automatic speech recognition system needs to analyze live human data, it can not be trained with machine-generated data. This can lead to inaccuracies and erroneous results.
2. Fills the gaps of generative AI
While generative AI tools can make human-like content such as photorealistic images and videos, they still have some issues that require human input. Let’s take the example of Google Gemini’s image generation tool, which created racially inappropriate images and was recalled.2
3. Fuels behavioral analysis
Behavioral analysis is an effective way of collecting qualitative data that is used for various business applications. Companies can use it to gain valuable insights into their customers, products, services, and operations. This allows them to make informed decisions that drive growth and profitability.
Behavioral analysis can not be conducted without human-generated data. For instance, if a retail store is observing the customers’ behavior as they enter a store to identify movement patterns, it needs to observe the customers in action. Such data can not be generated with human intervention.
Additionally, human-generated data can be used for predictive analytics tasks such as forecasting sales or predicting customer churn rates.
4. Makes the business more customer-focused
By leveraging human-generated data, companies can better understand their customers. This knowledge can then be used to create innovative solutions that improve the customer experience, optimize business processes, and develop new growth strategies.
Brands can create targeted marketing campaigns aimed at specific audience segments. Human-generated data is an invaluable asset for any business looking to stay competitive in the ever-changing digital landscape.
Top 4 barriers to obtaining human-generated data
1. Time-consuming
Data generated by humans takes more time than data generated by machines. This is mainly because people make errors, get tired, and take more time to do things than machines. For instance, AI-powered writing tools such as Jasper can produce content up to five times faster (the company claims) than humans.
2. Expensive
Human-generated data can be expensive since collecting, analyzing, and interpreting it requires the recruitment of contributors, expensive equipment, dedicated locations, and servers to store it. These costs rise with the size of the dataset.
For instance, to gather human-generated audio files, microphones and soundproof rooms will be required in addition to the participants.
3. Inaccurate
Human-generated data is highly accurate, but the level of accuracy starts to fall as the dataset becomes larger and the data collection process becomes more repetitive. Manual data collection can become error-prone since modern datasets are required to be large and diverse. Gathering such data involves repetitive tasks, which lead to mistakes and errors.
Such errors can lead to inaccuracies in the dataset, reducing the overall quality of the dataset, and can require excessive data processing. Check out this quick read to learn more about how to improve the quality of a dataset.
4. Sample bias
Human-generated data can also include sample bias. For example, the data might be collected from only certain areas or demographics, which may not accurately represent the population as a whole.
FAQ
Why is a crowdsourcing service good for human-generated data?
Crowdsourcing services excel in aggregating human-generated data, a cornerstone for the development of sophisticated AI models. This methodology leverages the intricate insights and varied perspectives of humans, thereby enriching the datasets that are pivotal for training AI systems. For instance, in nuanced tasks like sentiment analysis within word processing documents or facial recognition in videos, human annotations imbue a depth and context that machine-generated data may overlook, significantly enhancing the AI’s proficiency in simulating human cognition and responses.
Additionally, this human-curated data, ranging from text feedback to interpretations of audio files, is instrumental in sculpting comprehensive datasets that mirror the intricate, real-life challenges AI technologies confront. This process not only amplifies the quality and applicability of the training data but also fortifies the creation of AI models that are sensitive to the nuances of diverse environments. The intervention of humans in the data generation and refinement process aids in the detection and rectification of biases, a critical step in ensuring that the resultant AI models can function with equity and integrity across varied settings. Furthermore, by integrating big data and clickstream data, companies can harness actionable intelligence, which, when analyzed, provides significant insights for both the development of AI applications and the enhancement of data protection measures. This symbiosis of human and machine-generated data ensures a balanced dataset that is both rich in quality and expansive in scope, thereby setting a new standard in the realm of AI model training.
Further reading
- Crowdsourced AI Data Collection Benefits & Best Practices
- Data Collection Automation: Pros, Cons, & 3 Methods
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