Human in the Loop (HITL)

What is Human in the Loop (HITL)?

Human in the loop (HITL) is a design strategy that emphasizes the importance of human feedback in the development and operations of an artificial intelligence (AI) system. HITL acknowledges that AI and human intelligence each have their own strengths and limitations, and both types of intelligence are required to generate the best outcomes.


This is particularly relevant in areas like finance, transportation, and law enforcement, where AI-generated outcomes can significantly impact someone’s life.

The degree to which human-centered AI systems are implemented can vary greatly depending on the specific goals of the AI project, the complexity of the AI system, the risks involved, and the project team’s available resources. In some cases, HITL might only be used during the development phase of an AI system to supplement reinforcement learning.

Once the training data has been labeled and decision-making parameters have been established, human involvement may be limited to retraining initiatives, when required.

In other cases, human-machine collaboration might continue during operations to prevent AI drift and maintain optimal outputs as conditions change and/or new data becomes available.

The degree to which HITL systems are implemented can also be influenced by the regulatory landscape in which an AI system is deployed. In some industries, like healthcare or aviation for example, there can be stringent requirements for humans to oversee and validate decisions made by deep learning algorithms to ensure fair and equitable outcomes.

Examples of Human in the Loop Implementations

HITL implementation is not a one-size-fits-all solution. The nature of the task(s) an AI system is designed to complete can vary widely and necessitate different degrees of human involvement. Low-level routine tasks might be automated with only occasional human oversight, while more complex and/or sensitive tasks may require human intervention throughout the entire software development lifecycle (SDLC).

Lifecycle Human in the Loop
Initial Setup This phase of the design process defines the specific responsibilities of human operators, and sets clear boundaries for decision-making, task execution, and supervisory roles. It also includes the establishment of communication channels to support human-machine feedback loops.
Training Phase The use of human operators in this phase is especially when the AI system incorporates machine learning components. Humans can be used to label training data and provide feedback that will help teach the AI system to perform its designated tasks more accurately.
Operational Phase Human operators can be used to closely monitor the performance of the AI system. During this phase, feedback loops help the AI system to iteratively and incrementally improve its operations and optimize outcomes.
Continuous Improvement During this phase of the lifecycle, human operators may be involved in identifying opportunities for system enhancement and participating in retraining sessions designed to improve the AI’s precision and relevance.
Quality Control Human operators may be tasked with handling exceptions and uncertainties that the AI may encounter, and conducting audits to ensure the AI system’s decisions consistently comply with changing regulatory and ethical mandates.
Customer Interaction When AI-driven user interfaces, such as chatbots or virtual assistants, encounter challenges or limitations, human operators can be called upon to assist the conversational AI in real time. This is especially important in customer-facing contexts when the AI may not be able to provide a satisfactory user experience (UX) on its own.
Quality Control Human judgment can play an important role in maintaining quality control (QC) and providing quality assurance (QA). Human operators can be used to conduct audits manually, handle exceptions and resolve conflicts that the AI programming may encounter.

Benefits of Human in the Loop in AI

HITL can help prevent black box AI and improve AI accountability and transparency by adding checkpoints that require humans to review, interpret, and if necessary, correct or override AI decisions. The presence of a human element provides an additional layer of scrutiny, which can be crucial for explaining outcomes to stakeholders and maintaining trust in AI applications.

Limitations of Human in the Loop in AI

The addition of human oversight to an AI system doesn’t automatically solve all the issues related to transparency and accountability when AI makes poor decisions.

The way in which human input is integrated, the biases and expertise of the people involved, and the processes put in place to facilitate human-machine collaboration can all have significant impacts on the effectiveness of the human-machine partnership. If human interaction is required too frequently or if the process for interactions is too cumbersome, it can slow down the application, and potentially negate many of the benefits of using artificial intelligence in the first place.


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Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.