What is human-in-the-loop?
Human-in-the-loop (HITL) refers to a system where a human can give direct feedback to an artificial intelligence (AI) model. Humans can directly interact with such systems whenever the AI model returns a prediction with less-than-ideal confidence.
In tricky activities or circumstances when robots alone can’t produce the intended results, HITL acknowledges the significance of human judgment, decision-making, and oversight.
Humans participate actively in the AI process before, during, or after the automated system's operation. The main goal is to provide feedback, direction, validation, or supervision to improve the AI's performance, accuracy, and reliability. Some intelligent virtual assistants (IVAs) adopt this concept to provide more precise and accurate results.
Applications of human-in-the-loop
Many fields use HITL if a human’s confidence is needed in decision-making to achieve accurate, reliable, and ethical outcomes. Below are a few of the applications of humans in the loop:
- Content moderation. Social media networks frequently use HITL techniques to control user-generated material. In addition to automated moderation algorithms, people examine flagged or reported content to see if it is in violation of community standards or content policies.
- Customer support and chatbots. A chatbot can escalate a discussion to a human agent for assistance when it cannot comprehend or answer a customer's question. The human agent intervenes to offer individual assistance and manage difficult problems, enhancing the general experience.
- Telemedicine and medical diagnosis. A human expert is frequently included in corroborating diagnoses, analyzing results, and making wise treatment decisions. AI systems can assist in the analysis of medical images or patient data.
- Self-driving vehicles. In this case, even though the vehicle's AI system manages most driving responsibilities, a human operator or driver is prepared to step in when the system runs into ambiguous scenarios or doesn't react as expected. The person keeps an eye on the machine and takes over as required.
- Fraud detection. HITL is useful in fraud detection systems, particularly for financial organizations. Automated systems can flag questionable transactions or activity to avoid false positives or negatives. Humans then examine and validate these alerts. Human expertise is essential to spot intricate fraud patterns.
- Language transcription and translation. Language translation firms frequently use human-in-the-loop systems to increase translation accuracy. Human translators examine and amend the original translations produced by AI systems to ensure accuracy. Initial transcripts are made by automatic systems in transcription services, and human reviewers and editors check them for accuracy.
Benefits of human-in-the-loop
The core of today's commercial operations is AI and machine learning (ML) models. Companies use them as instruments to increase revenue, profit, and efficiency. In this manner, the main business advantage of ML algorithms makes the HITL machine learning model a significant subject.
- Data labeling. Machine learning with HITL benefits greatly from data labeling as it increases the operational efficiency of AI/ML models. Data labeling uses human contribution that improves the algorithm.
- High-quality results. The efficacy of AI/ML models is closely correlated with the data quality. More accurate data produces more precise predictions.
- Constant feedback. Despite the data labeling procedure, continuous feedback on HITL output improves the precision of ML models and guarantees the high caliber of HITL's production.
- Accuracy. Unlike AI, the human brain performs reasonably well when the data is incomplete or biased. For instance, a person can tell whether something is a cat or not by only looking at its tail. As a result, human input becomes a crucial component of HITL that boosts accuracy in case of incomplete data.
Best practices to implement human-in-the-loop
Businesses can combine the strengths of both humans and machines to achieve unprecedented precision and efficiency. It’s essential to strike the perfect balance between human and machine labor to maximize performance and production. Below are some points for businesses to keep in mind when implementing human-in-the-loop:
- Determine the right procedure. Look for repetitive, rule-based jobs that can be easily automated with robotic process automation (RPA). Think about the duties that call for human judgment and decision-making and seriously investigate procedures that are already outsourced.
- Summarize the roles of the human-in-the-loop. Define the functions and obligations for both people and machines and figure out which jobs will be automated and which will need human intervention. RPA can readily handle data validation and extraction, but humans are the best bet for critical thought or strategic decision-making.
- Educate staff members. Train staff members to use RPA and AI if users want the human-in-the-loop process to remain internal. Ensure they know the automated procedure and how to manage exceptions.
- Feedback system. Create a feedback loop involving people and machines. This makes it easier to verify that both automated processes and human decision-making are operating properly.
- Track progress. Regularly check on the performance of the automated process. It enables early detection and correction of any potential problems.
Learn more about supervised learning and understand how to teach machines to help us.
Sagar Joshi
Sagar Joshi is a former content marketing specialist at G2 in India. He is an engineer with a keen interest in data analytics and cybersecurity. He writes about topics related to them. You can find him reading books, learning a new language, or playing pool in his free time.
