AI & NLP

Human in the loop 101: what is it and why is it so important?

2 min read

Financial services firms have been turning to Natural Language Processing (NLP) solutions to extract valuable insights from vast amounts of unstructured data. But even the most advanced algorithms can’t match the intuition and creativity of the human brain. That’s where “Human-in-the-loop” (HITL) comes in. You might be wondering, what HITL is and why it’s so crucial to NLP solution efficacy? We’ve got you covered:

 

Consider an NLP solution as a car driving on a long journey. The car has lots of advanced features like automatic navigation and cruise control, which can make the trip smoother and more efficient. However, without a human driver to make critical decisions and react to unexpected situations, the car may get lost, encounter obstacles or even crash. In the same way, NLP solutions need human input to guide and adjust the automated processes, ensure accuracy, and prevent errors or biases.  Because, as Dr Gina Helfrich clearly points out in her recent article, AI can get things wrong sometimes. Human-in-the-loop provides essential insights and expertise to make the journey successful, correcting any mistakes to enable efficient machine learning.

 

By integrating human expertise into the NLP process, financial services firms can improve the accuracy and relevance of their insights. Humans can interpret complex linguistic nuances that algorithms struggle with, identify context and entities in the text that machines might miss.

 

But it’s not just about improving the quality of the insights. Human-in-the-loop can also help NLP solutions adapt and learn from their mistakes. It’s like having a personal trainer who gives you regular feedback on your exercise routine. As you work together, you can identify areas for improvement, adjust your technique, and achieve better results over time.

 

From a resourcing perspective, HITL means that you can get the most out of your investment in NLP solutions. Instead of having a team of experts working on tedious tasks such as annotation, they can focus on higher-level tasks like building more accurate models and designing new features.

 

In a nutshell, by integrating human-in-the-loop, financial services firms can achieve more accurate and relevant insights, and improve the NLP solution’s performance over time. Thanks to this continuous improvement, the earlier you implement, the better your solution will be compared to a competitor’s. 

 

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