Ensuring high-quality data begins with the collection phase. We can’t have a poor data source or collection technique and expect to have good & reliable data. Further, we cannot rely on incomplete and inaccurate data, which makes labeling challenging and gives rise to poorly-annotated data, to produce accurate predictions. If we start wrong, then it is almost a guarantee that we’ll end up wrong.

human assisted data collection

Data collection is therefore a process that is critical in the training and deployment of AI and machine learning (ML) models to ensure correct predictions. But, sadly, it is a process much overlooked. This is a misstep with huge ramifications in AI/ML.

There is no perfect antidote. But ensuring a good start helps. And that is possible with human assisted data collection.

Automated Data Collection: A Partial Solution

Data collection is a tedious, time-consuming, and unglamorous process—which is part of why it is often overlooked. But, thanks to advancements in ML, much of it can be automated.

Data gathering models can extract specific and varied data from multiple sources without or with little human involvement. They identify the sources from which to gather data and automatically retrieve them. This involves tasks like web scraping, accessing APIs, and interfacing with hardware sensors to extract data.

Automated data collection brings several benefits—not least reducing human effort.

  • Reduction of issues common in the manual collection, such as sampling bias, misclassification, and imbalanced datasets.
  • Saving time, frees up humans to focus on more critical tasks such as validation, thus improving the quality of the dataset.
  • Automation is efficient, and although the initial investment may be high, it can lead to a significant reduction in costs in the long run.
  • Real-time data collection is made possible with automated data collection tools.

Helpful as they are, automated data collection tools are far from perfect and from automating all aspects of the process.

They have several drawbacks. Below are some significant ones.

  • Lack of quality control and transparency;
  • Difficulty handling ambiguities and edge cases;
  • Potential for data overload leading to overfitting;
  • Failure to check and guarantee the credibility of the data source.

Because of their shortcomings—some of which are deeply rooted—human oversight and assistance are essential in ensuring the quality and relevance of the collected data. Human assisted data collection is ideal, with automated tools performing the menial & time-consuming aspects and humans handling the critical areas.

Why Human assisted Data Collection is the Answer

Automated data collection provides an answer to the data preparation problem—that of being ignored—by making it less tedious and more efficient. But it is only a partial solution. The complete solution—or at least a fuller solution—is to be found in human assisted data collection.

Human intervention ensures that the gathered raw data are reliable and representative of the real world—bar the biases—and that they are accurate and consistent. Human involvement also allows the incorporation of expert knowledge and keeping ethical concerns in check—crucial for making AI systems that are trustworthy and effective.

Let us try to understand how this is done by considering a few typical cases of human assisted data collection.

  • Data discovery and generation

Automated data collection tools can quickly scour data sources and retrieve them in their entirety. However, that is not ideal and can hamper the data collection and preparation process. The collected data may be irrelevant and/or dirty and so they could be a source of noise in the annotated version of the data—making the AI model befuddled and reducing its effectiveness. And not only that, volumes of unrelated data would put unnecessary strain on computational power, reducing its overall efficacy.

It can also result in imbalanced datasets, with certain classes of data over-represented and others under-represented. This would lead to the model becoming overfitted or biased. Oversampling of datasets can be particularly acute when generating synthetic data. This therefore risks introducing another set of problems on solving another.

In all these scenarios, human intervention is imperative—to define the relevant data and specify the problem, and rein in the tools from going overboard. And in cases where automated tools struggle, such as handling unstructured data, humans can provide assistance, making them more versatile.

  • Data labeling and validation

Gathering and augmenting data is but one aspect of data collection. The gathered data may not just lack diversity and depth, they may also lack sufficient details and context. Since machines have no ground understanding of the real world, enrichment, and annotation enable them to make sense of the data. This helps them gain understanding and learn better.

The collected raw data scarcely have any labeling. Automated tools can help with this essential task. They are trained with extensively annotated data, which enables them to make predictions and generate labels. Their capability and application, though, are limited.

Humans can enhance the capability of these tools. They can make on-the-spot decisions to facilitate accurate labeling and make nuanced judgments that automation tools struggle with. They are also essential to verify automated labels and ensure that they’re consistent and in line with the characteristics of the data.

Completely automated annotation is still a dream. For now, in the waking world, data annotation services provided by third parties or in-house teams remain indispensable for achieving high standards of quality and accuracy in annotated datasets.

  • Handling ambiguity and providing context

Automated systems are limited by the trained data—and this will always remain a constraint because the world encompasses more than just data. So, these systems will struggle or fail miserably at handling ambiguities or edge cases. Many are these scenarios.

This is fine so long as they are not left entirely on their own. Human assisted data collection ensures that these are not overlooked. Humans can provide context, help discern the subtleties, add understanding to cultural and linguistic nuances that may evade automated systems, and help address unforeseen and exceptional cases.

  • Ethical judgment and regulatory compliance

Ensuring that data collection is ethically sound and compliant with regulations entails more than just respecting privacy and security laws. It includes, among other things, selecting appropriate data, ensuring that the data sources are reliable and accurate and that they are free for public use, and minimizing biases by considering exhaustive and diverse data.

This applies to synthetic data. The generated data may reflect and exacerbate certain biases, which not only reflect but aggravate existing biases.

Human involvement can mitigate this. It can also ensure that data collection is done in an ethical, legal, and responsible manner. Where sensitive data are concerned, humans can implement necessary measures to safeguard the data and prevent unauthorized disclosure.

  • Expertise and feedback

Automated data collection tools have made the process a great deal less tedious and more efficient. However, they falter in several crucial areas and require human supervision and expertise.

Humans possess domain-specific knowledge and expertise that is crucial for understanding the intricacies and nuances of the data being gathered. Human assisted data collection ensures that data are acquired, interpreted, and processed with the standard of expertise and understanding that automated systems don’t possess.

Ensuring that the data gathered are relevant and of high quality is another aspect where human experts play a crucial role. The data further need to be reviewed and validated to make sure that they meet the required standards.

Humans also assess the performance of the automation systems and provide invaluable feedback on their performance, identifying areas for improvement and suggesting changes. This helps optimize the process and the systems improve with iterative refinement ensuring that they are more reliable.

Automation with Human Involvement

Automation of data collection is a boon, but we must not go overboard. Automated systems are primarily good at scraping data indiscriminately. Data collection involves much more than that. Humans, with their expertise and contextual understanding, remain indispensable—perhaps all the more so because of the widespread use of automated data collection tools.

There is a need to balance the automation of data collection and human intervention. Quick solutions are tempting, especially when time is scarce. But quick approaches can oftentimes lead to prolonged issues.

As automation proliferates, in data collection as well as in other areas, it is essential to keep in mind certain core values are not compromised: accuracy, clarity, unbiasedness, and trustworthiness. This necessitates constant human oversight and proper annotation to create accurate and representative training datasets.

This is by no means easy. You can however outsource data annotation to reliable third parties and get accurate data services. They combine automation with human expertise to collect and prepare data ensuring that they are accurate, complete, and consistent. This will further ensure AI/ML models are accurate and reliable.