Artificial intelligence is fast becoming all-pervasive, with companies across various industries using AI to deliver exceptional customer service, boost productivity, streamline operations, and bring home the ROI.
However, companies believe that implementing AI-based solutions is a one-time solution and will continue to work its magic brilliantly. Yet, that’s not how AI works. Even if you are the most AI-inclined organization, you must have human-in-the-loop (HITL) to minimize risks and maximize benefits.
But is human intervention required in AI projects? Let’s find out.
AI empowers businesses to achieve automation, gain insights, forecast demand and sales, and provide impeccable customer service. However, AI systems are not self-sustaining. Without human intervention, AI can have unwanted consequences. For example, Zillow, an AI-powered digital estate firm, had to shut shop because its proprietary algorithm failed to deliver accurate results.
Human intervention is a process necessity and a reputational, financial, ethical, and regulatory requirement. There should be a human behind the machine to ensure AI checks and balances are in place.
According to this report by IBM, the top barriers to AI adoption include a lack of AI skills (34%), too much data complexity (24%), and others. An AI solution is only as good as the data fed into it. Reliable and unbiased data and the algorithm determine the effectiveness of the project.
What is a Human-in-the-Loop?
AI models cannot make 100% accurate predictions as their understanding of the environment is based on statistical models. To avoid uncertainty, the feedback from humans helps the AI system tweak and adjust its understanding of the world.
Human-in-the-loop (HITL) is a concept used in developing AI solutions by leveraging machine and human intelligence. In a conventional HITL approach, human involvement happens in a continuous loop of training, fine-tuning, testing, and retraining.
Benefits of a HITL model
A HITL model has several advantages for ML-based model training, especially when training data is scarce or in edge-case scenarios. Additionally, compared with a fully automated solution, a HITL method delivers faster and more effective results. Unlike automated systems, humans have the innate ability to quickly draw from their experiences and knowledge to figure out solutions to issues.
Finally, compared with a fully manual or fully automated solution, having a human-in-the-loop or a hybrid model can help businesses control the automation level while expanding intelligent automation. Having a HITL approach helps improve the safety and precision of AI decision-making.
Challenges when implementing a Human-in-the-Loop
Implementing HITL is not an easy task, especially since the success of an AI solution depends on the quality of the training data used to train the system.
Along with the training data, you also need people equipped to handle the data, tools, and techniques to operate in that particular environment. Finally, the AI system should be successfully integrated into the legacy workflows and technologies to increase productivity and efficiency.
HITL is used to provide accurately labeled data for ML model training. After labeling, the next step is tuning the data based on the model by classifying edge cases, overfitting, or assigning new categories. In each step, human interaction is critical, as continuous feedback can help make the ML model smarter, more accurate, and faster.
Although artificial intelligence caters to several industries, it is used extensively in healthcare. To improve the efficiency of the AI tool’s diagnostic capabilities, it has to be guided and trained by humans.
What is Human-in-the-Loop Machine Learning?
Human-in-the-loop Machine learning denotes the involvement of humans during the training and deployment of ML-based models. Using this method, the ML model is trained to understand and reciprocate based on the user intent rather than pre-built content. This way, users can experience personalized and customized solutions for their queries. As more and more people use the software, its efficiency and accuracy can be improved based on the HITL feedback.
How does a HITL improve Machine Learning?
Human-in-the-loop improves the efficiency of the machine-learning model in three ways. They are:
Feedback: One of the primary purposes of the HITL approach is to provide feedback to the system, which allows the AI solution to learn, implement, and come up with accurate predictions.
Authenticate: Human intervention can help verify the authenticity and accuracy of the predictions made by machine learning algorithms.
Suggest Improvements: Humans are adept at identifying areas for improvement and suggesting changes necessary for the system.
Some of the prominent use cases of HITL are:
Netflix uses human-in-the-loop to generate movie and TV show recommendations based on the user’s previous search history.
Google’s search engine works on ‘Human-in-the-Loop’ principles to pick content based on the words used in the search query.
Let’s discuss your AI Training Data requirement today.
Myths of Using the Term “Human on the Loop”
Not everything about human-in-the-loop is rosy and reliable. There is serious contention among experts against those who call for more ‘human interference’ in AI systems.
Whether humans are in, on, or anywhere near the loop to supervise complex systems such as AI, it could lead to unwanted consequences. AI-based automated solutions are making decisions in milliseconds, which makes it practically impossible to have humans make a meaningful interaction with the system.
- It is impossible for a human to meaningfully interact with all the pieces of AI (the sensors, data, actuators, and ML algorithm) by understanding and supervising these interdependent moving parts.
- Not everyone can review codes embedded in the system in real time. The contribution of a human expert is required at the initial build stage and throughout the entire lifecycle.
- AI-based systems are required to make split-second, time-sensitive decisions. And having humans pause the momentum and continuity of these systems are practically impossible.
- There are greater risks associated with HITL when the intervention is in remote locations. Lag time, network issues, bandwidth issues, and other delays can impact the project. Moreover, people tend to get bored when dealing with autonomous machines.
- With automation growing by leaps and bounds, the skills needed to understand these complex systems diminish. In addition to interdisciplinary skills and an ethical compass, it is essential to understand the context of the system and determine the extent of humans in the loop.
Understanding the myths associated with the human-in-the-loop approach will help develop ethical, legally compliant, and effective AI solutions.
As a business trying to develop AI solutions, you need to ask yourself what “human-in-the-loop” means and whether any human can pause, reflect, analyze, and take appropriate action while working on the machine.
Is a Human-in-the-Loop system scalable?
While the HITL method is typically used during the initial phases of AI application development, it should be scalable as the application grows. Having a human-in-the-loop can make scalability a challenge as it becomes expensive, unreliable, and time-consuming. Two solutions can make scalability a possibility: one, using an interpretable ML model, and the other, an online learning algorithm.
The former can be seen more as a detailed summary of the data that can help the HITL model handle massive amounts of data. In the latter model, the algorithm continuously learns and adapts to the new system and conditions.
Human-in-the-Loop: The Ethical Considerations
As humans, we pride ourselves on being the flag bearers of ethics and decency. We make decisions based on our ethical and practical reasoning.
But what will happen if a robot disobeys a human order due to the urgency of the situation?
How would it react and act without human intervention?
Ethics depend on the purpose of what the robot is programmed to do. If the automated systems are confined to cleaning or laundry, their impact on human life or health is minimal. On the other hand, if the robot is programmed to perform critical and complex life-and-death tasks, it should be able to decide whether to obey orders or not.
The solution to this dilemma is acquiring a dataset of crowdsourced information on how best to train autonomous machines to handle ethical dilemmas.
Using this information, we can provide extensive human-like sensitivities to robots. In a supervised learning system, humans collect data and train the models using feedback systems. With human-in-the-loop feedback, the AI system can be built to comprehend socio-economic context, interpersonal relations, emotional inclinations, and ethical considerations.
It’s best to have a human behind the machine!
Machine learning models thrive on the power of reliable, accurate, and quality data that is tagged, labeled, and annotated. And this process is carried out by humans, and with this training data, an ML model is made capable of analyzing, understanding, and acting on its own. Human intervention is critical at every stage — providing suggestions, feedback, and corrections.
So if your AI-based solution is reeling under the drawback of insufficiently tagged and labeled data, forcing you to achieve less-than-perfect results, you need to partner with Shaip, the market-leading data collection expert.
We factor in “human-in-the-loop” feedback to make sure your AI solution achieves enhanced performance at all times. Contact us to explore our capabilities.