The AI-driven world we’ve been promised for years has arrived. Data-driven businesses are all turning to Artificial Intelligence to improve output. In fact, 45% of them say they’ve already integrated AI as part of their operations.
So humans are ready for AI. But is AI ready for us? One of the biggest challenges that AI faces at the moment lies around data quality. AI needs to process massive amounts of data to become useful, and that data needs to be accurate. Annotation errors could have tremendous consequences in the real world. So how can we improve data accuracy? First, we need to understand where the problem comes from.
The Challenge of Preparing Data at Scale
An effective AI system starts with high quality training data produced at scale. High quality training data is data that has a proper domain and user’s coverage, that is balanced, and has been annotated accurately and consistently. But the more data a business needs to produce, the harder it is to keep it accurate and consistent.
The best way to produce accurate and consistent data is through skilled, manual human anotación. This process enables the machine learning algorithm to learn much faster and with more precision. But to be reliable, it needs to process a lot of this annotated data. And the more data you produce, the more annotations humans need to create. That means companies must spend a majority of their AI resources not on the engine itself, but on the data needed to train it. In fact, companies dedicate 80% of their AI project time on gathering, organizing, and labeling data.
The Need for a Highly-Skilled Human Workforce
When we are teaching AI what a cat looks like, the skill needed to annotate the data is easy. But for most AI applications, the data is much more complex than that.
Take the example of an AI tasked to analyze X-ray images in healthcare. The humans needed to annotate the X-ray scans must have medical training and know how to recognize anomalies on such scans. That means the company must spend higher resources to either train its workforce, or hire medical professionals. And of course, the more specific the data, the more human training it will require.
Additionally, not everyone can become a high-skilled human annotator since it requires attention to detail, patience, dexterity, and the ability to understand the guidelines and apply them consistently.
The Necessary Compliance with Security and Privacy Standards
Quality is only one aspect of a clean training dataset. Ethics and security are two other components that are equally important.
A clean dataset should be designed to protect personally identifiable information, and limit its use unless necessary. For some use cases, such as facial or speech recognition systems, photos of human faces and recordings of human voices are required.
Improper use of personal data could infringe on people’s freedom, or worse, put them in harm’s way. Yet, some countries don’t have data protection regulations in place. That means that AI companies must regulate themselves and protect people’s privacy. But how can we make sure that all companies follow these same principles?
Aligning to privacy and security standards such as GDPR, CCPA, CPRA, SOC2, and DPA creates a strong foundation for delivering ethically and securely sourced datasets.
So What Can We Do?
The preparation, cleansing and annotation of training data is a complex but critical undertaking. Companies like Sigma provide businesses with out-of-the-box solutions to generate high-quality datasets quickly and efficiently.
Sigma has over 30 years experience in training data annotation, providing data accuracy north of 98%. Our highly vetted, trained workforce does not rely on crowdsourcing. Instead, we’ve adopted a multidimensional model to ensure quality of data at scale. What’s more? Sigma is ISO27001 Certified and 100% GDPR Compliant, with a long history of security and privacy compliance.
Success in AI starts with the right data preparation. Póngase en contacto con nosotros to speak with an expert and learn how to create the most advanced training datasets for your AI, today.