Building a Sustainable Data Annotation Strategy: Cost, Quality, and Time Management
Every image on the internet is annotated in some way or another for it to be identified. This is the way to recall and find out the difference between one image from the other. This is how we are able to tell images or items apart and search for objects in the first place. So data annotation or labeling is this process of tagging and discrimination for the purpose of evoking search results as well as training machine learning. However, I would suggest keeping in mind the critical issues of using AI in image training, which is notorious for its environmental degradation. This makes taking note of sustainable practices immensely important.
What Are Some Sustainable Data Annotation Strategies?
There are quite a few sustainability challenges that come along with this, such as the need for better laws involving the usage of authorized sources.
1. Initial Cost of training
Annotations are trained based on data and existing images. So, the training process is expensive. This includes the Cost of software, the right professionals, and labor for training. So this must be dealt with with the highest responsibility.
Quality control
Hallucinations are one of the biggest problems faced by AI. For images, any error can be catastrophic. Well, we all know that AI doesn’t have the ability or capability to comprehend things. As a result, we also receive wrong information, and it’s not sure that each time the information that comes up will be correct.
Over-reliance
Practitioners often think about overusing AI which leads to proficiency loss and errors in operation.
Data Security and Privacy
Data scraping is a sustainable image annotation conversion for AI. Storing and getting your annotated data processed by ML can lead to a risk of leaks and unauthorized usage and access of confidential information.
Devaluing of Human labor
A major counterargument for the integration of AI tools in annotated imaging is that it devalues the performance and the skills of human labor. With several companies trying to cut costs here and there, it becomes easy for professionals to be caught in the crossfire and laid off in masses.
Wrapping up
This brings us closer to some of the sustainable strategies of image labeling and annotation in the quality control, costs, and time management of imaging. While there are several ways in which accurate image annotation can turn out to be beneficial, the errors are too risky and can be highly cost-intensive . So, maintaining transparency as much as possible is important. Only then can we start to see some progress in the matter
