In the world of artificial intelligence, Data Labeling is an emerging field that is not yet widely known.
Data Labeling tasks involve assigning labels to various structured and unstructured data, in order to create a "semantic layer" - a set of information that Machine Learning or Deep Learning algorithms can understand. In a data-centric approach to artificial intelligence - which is the market trend - Data Labeling is an indispensable process!
In this article, we list 3 misconceptions about Data Labeling activities and their implementation to build AI products.
1. Data annotation is quick and easy to automate
If you've ever tried to label data internally, you can surely refute this sentence. The more data AI receives, the more accurate it will be. That's why it's important to provide massive, high-quality data sets. Annotating data takes several hours and is tedious work, which can quickly become frustrating for people who have never done it before, and disabling if they also have to carry out other tasks. Entrusting these tasks to a Data Scientist trainee is probably not a good idea...
Finally, even if progress has been made in automatic labeling, with ever more powerful platforms, this does not dispense with verification and qualification by a professional Data Labeler, who, unlike the machine, has functional and business experience in relation to the data to be labeled.
2. Precise data annotation is not essential
When it comes to developing high-performance artificial intelligence models, large quantities of high-quality annotated data are indispensable. Annotations provide precise information on data characteristics and labels, enabling machine learning models to generalize and make more accurate decisions.
However, if the data is inaccurately annotated or of poor quality, this results in errors and incorrect predictions on the part of the AI. These errors can take a considerable amount of time to correct manually, because although they may be rare in some cases, correcting them individually requires a great deal of effort. For this reason, it is essential to emphasize the quality of annotations, in order to minimize errors and optimize the efficiency of the machine learning process.
3. All Data Labeling outsourcing companies exploit their employees
Some data labeling companies exploit workers by adopting practices that contravene labor rights. Some of these companies, in a bid to cut costs, opt for unfair labor models such as crowdsourcing. This means that they call on casual and often poorly-paid workers, who carry out data labeling tasks in a fragmented and ad hoc manner, with expectations that are out of touch with the reality of these people.
What's more, these companies can also impose tight deadlines and excessive pressure on workers to produce annotations quickly, leading to stressful and precarious working conditions. Overall, the exploitation of workers by data labeling companies is a worrying reality that requires particular attention to ensure that workers' rights and dignity are respected.
At Innovatiana, we attach the utmost importance to fair compensation for our employees. We offer them stable employment and reject crowdsourcing. Our ethical concern as a company guides our choices.
We hope this article has changed your prejudices! If you're a CTO, Data Scientist, developer or just interested in Data Labeling, we'd love to hear from you!
