Enhancing and Facilitating Data Annotation for Behavioural Studies using AI

At Synaptosearch, our mission is to advance scientific research to improve global well-being. Across various scientific disciplines, data annotation serves as a fundamental practice. Scientists utilize it to deepen their understanding of the world or to train computers in making important observations.

However, data annotation presents significant challenges. Finding suitable annotators can be difficult as it can require expertise in specialized fields or uncommon languages. Moreover, the process can be time-consuming and tedious, diverting valuable resources from research endeavors. Cost is another barrier, especially when recruiting highly sought-after professionals. Additionally, the presence of inherent biases and disagreement among annotators can further complicate the annotation process.

Imagine a group of scientists trying to teach a computer to find cancer early in medical images. Even though they know a lot, they struggle to label the images correctly. This is because they need to find people who understand both medical words and how to look at pictures. It also takes a lot of time to go through all the available images. Additionally, not everyone agrees on what they see in the images. Because of these challenges, the data annotation process costs a lot of money and takes a long time.

Our solution aims to alleviate these challenges by using special algorithms tailored to important research projects. Our algorithms determine which data is most important to be annotated. This will speed up the data annotation process and make it less resource intensive. Additionally, our algorithms provide researchers with how ‘sure’ they can be about the data labels. Indicating that for some data, they might need another pair of eyes to mitigate bias.