The Importance of a Data-Centric Approach to AI
And Why Is The Concept of “Human-in-the-Loop” Important?
The pace of change today is faster than ever, driven by emerging innovations that hold tremendous potential. For a company to thrive in this climate, we must embrace technology thoughtfully but fearlessly.
Artificial intelligence and data analytics present new ways to understand customers, operate more efficiently and identify opportunities for growth. But unlocking the promise of these tools requires focus, investment and an openness to new ideas.
As we chart our course ahead, our ability to leverage AI and data will define future success. But people remain our greatest asset. By fostering a culture of experimentation, inclusion and human-centric design, we can develop solutions that provide real value.
The Need for High-Quality Training Data
One can get different answers to this question, but it is widely accepted that the most important piece of the AI and machine learning process is high-quality, secure, human-annotated training data.
There are different ways to collect and structure data, with different firms having their own approach.
The problem with using unstructured or raw data is that it is very difficult to train high-quality models. In addition, the annotations can have a large impact on the performance of a model if the labels are bad quality or if the model is trained in a biased way, unbeknownst to the engineers and data scientists building it.
In order to reduce the impact of these issues, it is important to use human-in-the-loop data annotations when training a model.
Why Human-in-the-Loop?
Human-in-the-loop data annotation, or labeling, has become an increasingly important part of the machine learning process.
The use of human annotations has brought out the best in machine learning algorithms by reducing the effects of bias in the data and ensuring that models are being trained in the most effective, and most accurate, way possible.
Human-in-the-loop data annotation has several benefits, two of which are essential to the training process:
Human-in-the-loop annotations are able to reduce bias in the data. In order to create a data set that is useful for training a model, it is necessary for the data set to include high-quality, diverse data, and this is not always the case.
Human-in-the-loop annotations help to reduce the chance of bias in the data that is being used, since the labelers are actively questioning and reviewing the data.
This means that the model will be able to learn from a variety of different perspectives and can be used to create more accurate predictions.
A diverse set of human labelers will be able to create more accurate annotations, since they will be able to provide different perspectives on the same data. This will help reduce the chance of under-fitting the model and will help increase the overall quality of the output.
This also allows the model to be used in a wider variety of situations, increasing its applicability and building on the idea of “AI for all.”
When all people, regardless of race, age, or gender, can use AI in their daily lives, we will be able to create a more inclusive and connected world.
By having a diverse set of human labelers, we are able to increase the number of people that we can use for data annotation, and this will create a better, more inclusive model that reaches more people and ultimately solves more problems.
What can AI teams do to find support?
Different AI and Machine Learning Data Service providers recognize the many benefits of using the human-in-the-loop data annotation model.
These providers are used to supporting AI models, and some have networks of thousands of contributors that can help diversify an AI training process.
A large, diverse team of data annotators can help reduce bias in datasets, so it is being trained in the most effective way possible.
At every company, data and AI will play an increasingly crucial role. But the true key is still people. With a spirit of creativity, inclusion and fearless experimentation, we can leverage innovation to create positive impact for customers and communities alike. Onward!