Machine learning (ML) has come a long way, spawning a vast array of helpful applications—from virtual game players to bread (and, later, cancer) identification.
Standout programs such as those are still quite rare, but a rapidly growing number of organizations nonetheless benefit from AI tools through the process of human in the loop machine learning.
The term “human in the loop” (HITL) refers to the process of involving humans in various stages of ML model training in order to provide more useful, reliable, and consistent results. Using HITL is a relatively accessible way by which businesses can adapt existing AI models for their own specific uses.
So what is human in the loop machine learning, and how does it impact various industries today? Let’s delve deeper into this interesting topic!
- Human in the loop (HITL) AI is an approach to ML model development that involves humans at various stages in the process, including data preparation, testing, and outcome evaluation.
- HITL results in more consistent, reliable predictions and judgments. It’s also a valuable resource in combating bias in AI and thereby mitigating potential harm.
- Outsourcing machine learning human in the loop is a viable and cost-effective option, as many of the tasks can be done remotely through on-demand work marketplaces or tech agencies.
- Human in the loop machine learning tasks include data annotation, labeling, classification, and transcription. All of which enhance the quality and efficiency of the model training process.
What is Human in the Loop Machine Learning?
Let’s get a bit more specific.
Machine learning models, to rather simplify things, use statistics and algorithms to make judgments about certain inputs.
For example, large language models study examples of text or speech to synthesize their own compositions, while computer vision models use various visual cues to identify objects in pictures or videos.
To come up with judgments or predictions, ML models need to be fed large volumes of data: examples from which they can establish patterns and extrapolate or derive their conclusions.
They also need to have relevant categories programmed into them and to have their output verified, especially where edge cases may be involved. HITL machine gets humans involved at all these points—and more—to provide direct input or feedback, which enhances a machine learning model’s confidence and reliability.
Human Expertise in Model Training
Human judgment plays a few main roles, broadly speaking, in a HITL setup.
For one thing, because machine learning models are based on statistics and algorithms, the kind of certainty they achieve is different from those of humans. Its confidence is measured proportionally to what it already knows—granted, quite a lot—but it can’t assert certainty over areas where the data is ambiguous.
Humans familiar with the subject matter can make up the difference, improving confidence by affirming correct decisions and rejecting wrong ones while also clarifying edge cases that don’t have much representation in extant data sets.
In terms of the bigger picture, humans are also necessary to determine whether a model’s output is even useful. This means setting up and refining categories or types of output to ensure they stay relevant to the tool’s end users.
And finally, human in the loop machine learning can help identify what humans would understand as problems, but that AI would not.
One major instance of this is correcting for bias in ML models. Data sets can reflect long-standing social problems, for instance, but without human guidance, ML tools may end up perpetuating rather than correcting such disparities.
In other words, human involvement in AI tools makes them better for human use. And ultimately, that’s what AI tools should be striving for.
Machine Learning: Human in the Loop Tasks
There are a lot of ways that humans can get involved in ML model training, from day-to-day data prep work to big-picture project leadership. Here are a few of the basic but impactful tasks that are most often included in the process.
Data Annotation and Labeling
In order for a model to make sense of the data it’s processing, that data needs to be prepared for its use.
Data labeling and annotation are means by which different forms of data—like videos, images, or written texts—are made comprehensible to the ML models using them. Basically, it involves tagging a piece of data, or some part of it, with a term that the model can understand.
A pastoral image, for example, might be classified as such overall, while individual pieces might be marked for what they are: grass, cow, fence, tractor, etc.
Businesses training their own AI systems often start with new data sets or nonstandard labeling schemes. By labeling or annotating data beforehand, they can provide higher quality input, making the process more efficient and the outcomes more accurate to their goals.
By implementing custom labels or tags, HITL workers enable businesses to turn their unlabeled data sets—whether purchases, simulated, or compiled themselves—into useful input to train models for new uses.
Data Classification
Classification is a supervised machine learning process in which a model attempts to predict or sort a subject’s class—its label or category—based on data it’s been trained with up to the point of testing.
During data classification, human in the loop workers can assist by developing training situations, evaluating predictions or outcomes, and adjusting decision confidence.
At a higher level, experts can also be tapped to refine the classification schemes a model uses: making them more specific, more clearly delineated, or more applicable to their use in more complex processes.
Data Transcription
Data transcription is the practice of taking non-textual data, such as video, audio, or images, and rendering them as text while focusing on particular themes, subjects, or concerns.
In this way, the model receives data that is focused on the subject at hand. It’s especially useful in qualitative applications, where models need to work with the ideas or meanings humans associate with certain kinds of information.
HITL data transcriptionists create useful, textual training data in large volumes to facilitate the training of AI models in these qualitative, subjective fields.
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Benefits and Advantages of Human in the Loop
Incorporating humans into your model training process doesn’t just provide better outcomes—it can also improve the process itself. Here are some benefits a business can reap from a human in the loop machine learning approach:
More Efficient Learning
Properly labeled, annotated, or transcribed data makes for better input data quality. This, in turn, reduces the burden on other steps of the model training process—which makes for more efficient development.
Data preparation is best viewed as a task in itself, which merits the proper investment of time and resources. Getting people consistently involved in this process ensures that the continuous work of model training can proceed smoothly and efficiently.
Cost Optimization
What would be the alternative to human in the loop machine learning?
It’s theoretically possible to have processes like data labeling, annotation, and classification be automated. But you’d still deal with the same fundamental weaknesses—amplified, as it were, by the lack of human checks and moderation.
At the very least, HITL involves the costs and challenges of finding models that can perform the data preparation and moderating tasks you need done.
It could take a lot of time and effort on its own. But perhaps more to the point, it opens up possibilities of costly errors, poor decisions, or even penalties, should your model end up making the wrong judgments.
Enhanced Accuracy and Reliability
One of the principal goals of the human in the loop approach is ensuring that model output is reliable and consistent. The multiple points of human intervention—from data preparation to testing to continuous improvement—ensure that you get more accurate, precise results than you would otherwise.
Adaptability and Flexibility
Using machine learning human in the loop opens up a lot of possibilities that might otherwise be closed.
You can adapt existing data sets with old labels or even create useful inputs out of otherwise unsorted information. You can transcribe data at scale, rendering it viable for use in ML model training.
With relatively low costs, HITL presents an accessible way for businesses to customize existing models and adapt them to changing circumstances—rather than being confined to already extant tools or data sets.
Ethical Considerations and Mitigating Bias
Machine learning human in the loop has the potential to multiply our capabilities, but it’s also at risk of amplifying harm. Models that implement existing biases at scale can have devastating effects, such as endangering their health, compromising their finances, or locking them out of opportunities for work or education.
As AI models are, ultimately, tools, their output remains the responsibility of humans. Involving humans in critiquing and correcting such tools is a baseline ethical imperative, and HITL presents an effective way of doing so.
Outsourcing HITL Machine Learning
HITL model development can easily be broken down into discrete tasks, many of which can easily be done remotely. It makes outsourcing a viable, cost-effective option.
Many of the tasks involved in model training—such as data annotation, data labeling, data classification, and data transcription—are relatively easy for people to train for.
So while quite a few AI development roles may be difficult to source, you’ll find it quite easy to outsource human in the loop positions through channels, such as:
- On-demand work marketplaces
- Human in the loop enterprises
- Tech companies
- Outsourcing agencies
So once you’ve identified tasks to outsource, channels for collaboration, and point persons on your team—you can go ahead and get started!
Get Your Own Human in the Loop Machine Learning Team with Magic
Through Magic, you can assemble a remote HITL model training team chosen precisely to suit your company’s projects. Just tell us what tasks you want to be handled, and we’ll find you the right candidate or team in no more than a week.
Our services are designed for speed and flexibility. Whether you need a single part-timer or a full remote team, we can handle it for you. And if at any time your needs change, you can adjust your services with a quick call to us.
Join the AI revolution with Magic’s remote human in the loop model training teams tailored to your project’s specific requirements. Learn more about quicker development and more dependable AI and machine learning models when you download our HITL outsourcing eBook!
With a remote AI services team, you’ll be well-equipped to optimize your AI models at every stage of the development process. Schedule a call with us today and unlock the potential of the human in the loop machine learning approach!
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