3 misconceptions about Data Labeling

💡 In the world of artificial intelligence, Data Labeling (“data labeling” or “data labeling” in French) is an emerging field that is not yet known to everyone.

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Data Labeling tasks involve assigning labels to various structured and unstructured data in order to create a “semantic layer”, which is 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!

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In this article, we have listed 3 misconceptions about Data Labeling activities and their implementation to build AI products.

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1. Data annotation is quick and easy to automate

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If you have already tried to label data internally, you can surely disprove this sentence. The more data the AI receives, the more accurate it will be. It is therefore important to provide massive and quality data sets. Annotating data takes several hours and is a tedious job, which can quickly become frustrating for people who have never done it before, and disabling if these people also have to perform other missions. Entrusting these tasks to a Data Scientist intern is probably not a good idea...

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Finally, even if progress has been made in terms of automatic labelling, with ever more efficient platforms, this does not exempt from verification and qualification by a professional Data Labeler, who, unlike the machine, has functional and business experience in relation to the data to be labelled.

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2. Annotating data accurately is not essential

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When it comes to developing efficient artificial intelligence models, high-quality annotated data in large quantities is essential. Annotations provide accurate information about data characteristics and labels, allowing machine learning models to generalize and make more accurate decisions.

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However, if the data is annotated inaccurately or of poor quality, this results in errors and incorrect predictions on the part of the AI. These errors can require a considerable amount of time to correct them manually, because while they may be rare in some cases, correcting them individually requires a great deal of effort. That is why it is essential to highlight the quality of annotations, in order to minimize errors and optimize the efficiency of the machine learning process.

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3. All Data Labeling outsourcing companies exploit their employees

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Some data labeling companies exploit workers by adopting practices that go against labor rights. Some of these companies, in an effort to reduce costs, are opting for inequitable work models such as crowdsourcing. This means that they use casual and often poorly paid workers, who perform data labeling tasks in a fragmented and ad hoc manner, with expectations that are de-correlated with the reality of these people.

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Additionally, these businesses can also impose tight deadlines and excessive pressure on workers to produce annotations quickly, resulting in stressful and precarious working conditions. Overall, the exploitation of workers by data labelling companies is a worrying reality that requires particular attention to ensure that the rights and dignity of workers are respected.

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At Innovatiana, we attach paramount importance to the fair remuneration of our employees. We offer them stable jobs and we reject the use of Crowdsourcing. Our ethical concern as a company guides our choices.

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💡 We hope that this article was able to change your prejudices! If you are a CTO, Data Scientist, developer, or just interested in Data Labeling, feel free to make an appointment with us !