Businesses that want to increase profits using ML and AI must pay attention to the accuracy of the data labeling process.
While many are conscious of how the convenience and speed that machine learning offers can help make their business operations more efficient, less attention has been given to the losses that could be incurred if their data sets have been labeled with poor accuracy.
ML is not magic. It’s a technical process that involves developing a model through pattern recognition and the phrase “garbage in, garbage out” has never been more relevant than with machine learning. Simply put, poorly labeled data results in a model that makes a higher number of mistakes, resulting in losses.
One of the most crucial potential losses is monetary loss.
Here’s an example: If a model that has been trained to detect ripe apples in an orchard does not meet the acceptable accuracy levels, it is much more likely to miss ripe apples that should be picked. In the U.K, there have already been losses of roughly 16 million apples in 2019 due to lack of harvesting capabilities.
These are apples that could have been sold for profit. For smallholder farmers, losses like these could make or break their operations, especially if their ability to provide a constant supply to supermarkets come into question.
Farmers who are at risk of losing their contracts with buyers would likely decide to switch to a different computer vision company who could provide machines with higher accuracy levels. He needs a service provider who can guarantee an accuracy of at least 85-95%.
To achieve this, it’s vital for a service provider to obtain high accuracy training data sets. Having access to this will allow the company to establish its reputation as one that can provide highly accurate algorithms for highly accurate machines.
Companies that fail to do this would likely lose out on business that goes to their competitors with more accurately trained models. It’s an opportunity cost that would easily be avoided by having high quality labeled data.
Common reasons for low accuracy labeled data
To understand what makes up high quality data, one must first grasp how data annotation is conducted and the issues that lead to inaccurately labeled data sets.
At this early stage of machine learning, the initial processing of data is manual and may involve actions like data annotation, data transcription and sentiment tagging. It is laborious human work that requires an immense attention to detail.
Besides putting a strain on the cognitive load of the person responsible for labeling, the process also leaves room for prejudicial bias that occurs because of stereotype influences or cultural contexts. As data volume grows, the difficulties in catching mistakes only increases.
This is why it’s so important to have data labeling standard operating procedures compliant with quality control best practices.
Obtaining high accuracy training data sets
Some businesses may consider having their in-house team working on data labeling as an effective quality assurance measure, especially because the team is more likely to be familiar with the materials being labeled. But high-quality data labeling is not always correlated to familiarity.
More often, it’s about the ability to set up stringent workflows and rigorous quality control methods. Setting these up is not always cost efficient and may not be the best use of human resources that could be better spent on the actual development of algorithms.
The more efficient solution is to look for a dedicated data labeling partner that provides high quality, accurate training data sets to use for training AI and machine learning models.
A reliable partner should have a team comprising individuals that have been hand-picked and trained to deliver high precision.
They should also have a workflow that takes into consideration issues such as quality of collected data, prejudicial bias, and a review system that is rigorous enough to attain high levels of accuracy.
Companies that specialize in data labeling would have quality assurance measures already in place to do this and could set up ground truth and consensus scoring processes to ensure that their data annotators perform at the highest levels.
For a business to succeed with machine learning, high quality data is crucial. But if it wants to scale, if it wants to get to the next level, having a strong partner is imperative.
Mark Koh, chief executive officer of Supahands, wrote this article. The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Photo credit: iStockphoto/kjekol