What exactly is a human-in-the-loop and what is behind the name of our company?

“Human-in-the-Loop” is a term which is well-known in different fields of engineering and computer science and has been widely used in interactive simulation models in aviation, driving, and robotics. In such simulations, humans play an important role because they influence the simulated environment with their own actions.

In recent years the term has found wide adoption in the field of Artificial Intelligence, where it basically denotes AI systems in which human and machine performance jointly contribute to improving the overall results and accelerate the learning process. Such systems usually involve a continuous interaction between the human and the machine in order to train a model and then monitor and update it once it’s deployed.


1. Definition

Image shows definition what is HITL

In other words, this is the process of combining machine and human intelligence to obtain the best results in the long-term. This can be used during the training and testing phases of an AI model in order to create models with higher accuracy more quickly and efficiently but it’s indispensable during deployment phase as well. Especially for AI models that are being deployed “in the wild”, there is a very high chance that they will come across situations which they are not prepared to handle because they are under-represented or mis-represented in their training data. In such cases, models will require human intervention so that humans can verify the predictions of the AI model and send that feedback to either replace the AI-generated prediction or to be used for future re-training and fine-tuning of the model. 


2. How it works

The aim of human in the loop is optimizing models and algorithms through human intervention and contribution, to create better and more accurate AI. As we mentioned, human-in-the-loop can be applied at various stages of the AI lifecycle:

  1. Training and testing: Humans in the loop can be involved during model training, validation and testing in order to accelerate the learning process. Humans can first demonstrate how tasks should be performed and afterwards provide feedback on model performance. This can be done by correcting the model’s outputs or evaluating them, which creates a reward function that can be used for reinforcement learning. Learning from a combination of human demonstrations and evaluations has been demonstrated to be faster and more sample-efficient when compared to traditional supervised learning algorithms. 

  2. Deployment: Human in the Loop workflows are especially important when the availability of training data is very limited or the data is imbalanced or uncomprehensive – so we are unsure whether the model is prepared to handle all potential edge cases. In addition, even if the model usually achieves high accuracy, human monitoring and double-checking might be needed if model mistakes might end up being very costly: for example, in cases such as content moderation of user-generated content where false negatives may result in irreparable damage. In both cases, the model can be connected to a labeling interface where outputs below a given threshold of certainty are routed so that they are checked and verified by a human, either in real-time or in batches for future re-training. 
As the world continues to evolve around us, AI models will need to be kept updated with current data in order to avoid model drift and harmful biases, and hence humans in the loop can curate and annotate further data to feed back into the model.
Image shows workflow diagram in HITL

3. Hiring: human in the loop

Here at Humans in the Loop, we have taken this technical term and we have given it an additional meaning: integrating humans into the workforce and into the digital labor market. As a social enterprise, we use the plural of “humans in the loop” so that the name does not refer to a single person but rather a collective working together to power some of the most exciting applications of AI. 

Our workers are not just annotators or labelers, they are professional humans in the loop who have worked on a variety of AI projects and have developed an expertise on how AI model training works, what is considered an edge case, what data might confuse the model, and what data might cause harmful biases. After collecting and/or annotating the training datasets for the model image by image, these humans in the loop have a deep understanding of what the data looks like and why the model might be exhibiting certain errors when deployed in real-life scenarios. 

That’s why working with dedicated teams of humans in the loop is the future of trustworthy vision AI, and we won’t be surprised if in the future a “human in the loop” becomes an actual job title that companies start hiring for! It is always said that AI will take away a variety of human jobs but it will also create a myriad more. We are convinced that there will be a special need for trained humans who are able to supervise and monitor AI models and ensure Artificial Intelligence is safe, reliable and bias-free for all of us. 


Hope this was helpful! If you are interested in implementing a human in the loop in your AI pipeline, get in touch with us and we would be happy to have a call.

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