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In Defense of Humans - the Fact vs. Fiction of Labeling Automation


The capabilities of machine learning (ML) and artificial intelligence (AI) platforms are constantly evolving, and we spend a lot of time planning, building, and iterating toward a more automated future. However, this forward looking approach can obscure what is fact and what is fiction when it comes to automation and its role in model development today.

That’s why we’ve written In Defense of Humans - the Fact vs. Fiction of Labeling Automation.

In this piece, we address some common myths and questions about labeling automation:

  • Why can’t your ML platform automatically label my data?
  • Can’t you use labeled data from the same use case to automate my data labeling?
  • What role do humans play in the labeling process?
  • Isn’t automation more efficient?
  • What types of automation do exist?
  • Are there industries/use cases where significant automation is possible?

For most industries and companies, focusing on increased data quality through human effort will prove more efficient and cost-effective in the long run than focusing on automating the labeling process. 

Alegion operates at the intersection of machine and human intelligence - we are one of the few companies both building the tools for annotation and developing and managing a skilled workforce which gives us unique insight.

We hope this explainer helps you understand better both the limitations and the extraordinary power of machine learning platforms.

We hope you enjoy the complete white paper here.

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