AI annotation writing jobs – How to use AI data annotators

How to use AI for data annotation.

AI-driven automation for data entry tasks.

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AI annotation writing jobs are a sign of the times. Data entry jobs have long been an expected early casualty of the artificial intelligence boon. However, where one contractor is mourning the loss of their job security, another is learning how to use machine learning to their advantage. Perhaps that why you’re here? Assuming so, what are AI annotation jobs and how do you get hired for one?

What are AI annotation writing jobs?

AI annotation writing jobs are data entry jobs, which previously would have been performed by detail-oriented independent contractors. In short, it’s an early casualty of the AI revolution. After all, who understands computers better than… computers?

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The manual entry of data into a computer system, such as a database, is a very systematic and often tedious task. In an ideal world, we’d hope that the humans previously working these jobs suddenly have the freedom to chase a creative and fulfilling career! But the reality is not so simple. For starters, we can’t just flagrantly assume that no-one enjoys their data entry job.

That aside, the accuracy of AI algorithms in data labeling is a benefit to many employers. Suddenly human error is a thing of the past (especially important for the inherent biases in how humans collect data) – or is it?

How to get AI annotation jobs

AI annotation jobs are available online. Flexible work and remote work have become extremely popular since the global pandemic, with many individuals realising for the first time that they work better from home. Annotation jobs employment is in jeopardy, however, with many businesses finding machine learning solutions that are faster, cheaper, and more reliable than their human counterparts.

How to use AI data annotators

The expertise of human contractors is slowly (or not so slowly?) being replaced by AI. Does artificial intelligence really understand the role as a human does? Sure a computer system can change 0’s to 1′ faster than a human but does it know why it’s doing it? Well, that’s a deeply philosophical question to answer, and the answer is no.

Understanding labels is what leads to the insights that businesses are hiring for. To use AI data annotators effectively, it pays to understand the industry and your KPI’s (Key Performance Indicators).

Incredibly, AI is proving that it can in fact handle this conceptual task – at least well enough pass a narrow ‘Turing test’ of “Is this useful work?” So where else does AI have the advantage? Humans have inherent biases. Personal identities such as sexual orientation, national origin, conversational fluency and many many more details can impact data analytics differently from analyst to analyst, due to each analysts differing internalised biases. Diversity is a pertinent issue not only in data analytics, but also in the way in which we believe AI is capable of performing it.

A data annotation platform uses reliable training data on the latest trends to provide data-driven services for any number of industries. This includes HR, advertisements, and financial services. Skilled professionals can use AI data annotators to enhance their role, rather than be replaced. Quality assurance, excellent writing, and high quality work are always important in the text annotation process. However, integrity is harder to hire fore – especially if your new hire is not a human.