Why human-in-the-loop matters for data quality

Striving for both efficiency and completeness

Byoungjoon Min
Why human-in-the-loop matters for data quality

What is human-in-the-loop?


Artificial intelligence is revolutionary enough, but errors due to variables cannot be avoided. To overcome this dilemma, we at DataHunt have adopted a human-in-the-loop (HITL) model in our data labeling process.

Of course, AI was developed with the goal of being able to think like a human on its own, but it is still difficult to handle everything from start to finish by itself. After all, it is the human, not the AI, who is in charge of choosing the algorithm, accepting the results, and giving feedback.


Let's say an AI analyzes data and presents a 99% accurate result to a human without any explanation. You might decide that you don't trust the algorithm's dialog 100%.In the end, a human touch was needed to minimize the variability of AI and ultimately make it commercially viable. Human-computer-interaction (HCI), which centers on the interaction between AI and human computers, came into focus.

Continuous human-computer interaction. Human-in-the-loop (HITL) is also a subtopic of human-computer-interaction (HCI). DataHunt has chosen to use it to increase accuracy and confidence in its data.


The future with AI and its limitations


AI technologies are already in our daily lives. Take the case of self-driving cars, for example, which are categorized into levels 0 through 5. In the U.S., Level 4,"highly automated driving," has already been realized, with no reported technical issues.

And many countries are pushing for the commercialization of autonomous driving and the development of "fully automated autonomous driving" at Lv. 5, i.e., systems that can drive without a driver.



However, there are still many hurdles to overcome before it can be easily commercialized. On April2, 2022, the self-driving car "Cruise" operated by General Motors(GM) was pulled over by police for failing to turn on its high beams at night while parked. However, the self-driving car fled when the police approached.This was a mistake because the AI didn't yet have an algorithm for what to do when it encountered a police officer, so it perceived it as an obstacle.


Similarly, AI is not yet able to produce perfect results. They need human experts to make intermediate adjustments, which is where human-in-the-loop comes in. It's a way for humans to check various variables to ensure the data is complete.


The answer is human-in-the-loop


Human-in-the-loop(HITL) refers to the process of human experts checking intermediate results and adjusting training data during the AI training process. While it is common to wait for the AI to produce results on its own during the training process, the difference is that a human intervenes in the middle to form a kind of feedback loop.


To put it simply, when using AI for data processing, a human inspects the data pre-processed by the AI. Then, once again, the AI performs post-processing, which reduces data processing time and cost while increasing accuracy.



Human-in-the-loop example


Let's consider a human-in-the-loop approach to self-driving cars. If a single lane of traffic is blocked, the AI will only learn and teach the behavior "stop". If a human intervenes and can confirm that there are no cars in the lane on the other side of the centerline, it can temporarily cross the centerline and continue on its path. As humans check and feedback on the AI's learning, it can perform better.

Human-in-the-loop isa way to improve performance by intervening with experts in situations where AI learning is difficult to judge or inefficient, so that reliable learning results can be derived.


At Datahunt, we dream of a time when AI technology will be commercialized and easily accessible, which is why we have adopted a system of accurate and fast data labeling and human-in-the-loop interaction. We will not let go of our development research to make data technology more accessible with the help of humans.


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