Annotation Services

How Human-in-the-Loop Boosts Performance of AI-driven Data Annotation?

  The need for accurately annotated data

With all the buzz around artificial intelligence (AI) and machine learning (ML), we must remember that in training any AI model, a large amount of data is required. The source of this data must be accurate for the success of the AI model. Apart from that, the available data should be accurately annotated for the AI model to be successful. Data annotation involves adding labels or metadata to raw data to make it understandable for machines. Behind every AI and ML system, it is data annotation that helps the models understand the context of the data that is available.

It goes without saying that the data annotation needs to be consistent and of top quality for any AI model to be effective. A lot of the data annotation done these days is with the help of AI. But not all is perfect in the world of AI-driven data annotation.

Limitations of AI-driven data annotation

If the quality of data annotation is poor, it can have adverse effects on ML and AI systems. Machine learning could make wrong assumptions if the data is not properly annotated.

In fact, it has been observed that high-quality data annotation is possible mainly with human intervention and feedback. That is precisely the reason why human-in-the loop is found to be an absolute necessity in order to fully exploit the power of data. When humans are in the loop, they are able to apply nuanced judgement and contextual knowledge to accurately annotate data sets. It is to be noted that many of the data annotation tasks such as video annotation, sentiment analysis, named entity recognition, and creative captioning for image annotations require humans to verify the labels to ensure accuracy.

In this case the humans-in-the-loop (HITL) are the human specialists such as data annotators, data scientists, machine learning engineers and even product and project managers who are involved from the planning stages of the model development and supervise the learning process of the machines.

How does human-in-the-loop (HITL) work?

HITL involves using the power of both machines and human intelligence to create high-quality ML-based AI models. In cases where a machine is unable to understand and solve a problem, human intervention could help. Humans label the data which is then handed over to  ML algorithms to learn and make decisions. This creates a continuous feedback loop that allows the ML algorithm to give better results every time.

So, what really are the benefits of human-in-the-loop in machine learning?

Automated systems could make mistakes especially if the available data is ‘noisy’ or complex. Humans are able to review the automated annotations and make corrections wherever needed. This helps in the overall improvement of the model’s ability to understand the data and make corrections and changes if any inaccuracies are noticed. Humans can contribute their expertise through a feedback mechanism, thus helping the  algorithms to improve themselves.

Therefore, with a human-in-the-loop setup, humans are able to actively get involved and influence the learning process of machines. Getting humans involved with the data annotation process helps in the building of high-quality labelled datasets. Humans are able to analyse complex situations and make informed decisions, leading to reliable ML models.  With human feedback available, applications that use AI improve at a faster pace and are more effective at training than if they were doing the training themselves. This improves the  performance and dependability of AL/ML models. Apart from this, the human-in-the-loop approach is essential for building confidence and responsibility in AI systems.

Another advantage of HITL is with the handling of edge cases.  Edge cases are situations where an ML algorithm is presented with scenarios it has not encountered before. These are situations that normally do not happen, and yet need to be planned for especially in areas such as autonomous driving systems, where even a small margin or error could end up in injuries and even fatalities. Humans are much better equipped to handle edge cases than automated systems.

The path forward

According to Gartner, ‘human-in-the-loop’ solutions will be a part of the 30% of new legal tech automation offerings by 2025. It is clear that integrating humans into the machine learning pipeline helps with better training and validation of AI/ML models. With HITL, organisations can truly and fully harness the power of the data that is available with them. This paves the way for a brighter future for artificial intelligence and machine learning development.

How IBPM can help

At Infosys BPM, we help our client’s data science teams through ourAI Annotation Services. We enable the building of training data of the highest quality for AI at scale. We use a platform plus human-in-the-loop service model that saves time as well as resources for the teams, and it focuses on strategic priorities like improving and refining the AI model itself.