Human in the loop explained

If your business has been seeking the advice of software developers in automating any of your business operations, they may have already used the phrase “human in the loop” to describe the process by which they will design your bespoke software to ensure that your business solution effectively addresses your company’s tech challenges.

It is also sometimes referred to as artificial intelligence (AI), a similar machine learning process.

The question is why this is important for your business efficiency and viability? In our experience, understanding this balance between automation and human oversight separates successful implementations from costly failures.

Biewald argues that AI models that do not have some sort of human-in-the-loop element are flawed. Why? Because the AI naysayers (or the people selling supplementary crowd-based services as in this case) say that accuracy of AI tops out at around 80%.

— Adrian Bridgwater (Forbes, as of 2016)

What is human in the loop (HITL)

The simple definition of HITL describes the process when the machine or computer system is unable to offer an answer to a problem, needing a human intervention.

When this occurs, this additional data incorporated in the decision-making process is then added to the computer’s algorithms to perform a specified operation in future automatically. The software program is developed for that specific business situation or a generalized business model.

CEO of CrowdFlower, a data management and tech development company, Lukas Biewald, describes the ideal software development process using the ‘human in the loop’ business development model as follows:

1. Firstly, the computer machine learning model gets access to any relevant data, whatever the format, including video, image or document for appropriate labeling. Labelling of component parts of a process is vital for the developer to put together the methodology in the computer’s programming language, which includes labels their algorithm can understand.

This is a little like Google’s indexing system which enables appropriate website display when humans enter searches into their search engine. The Google ‘bot’ will have scanned all websites on the net for appropriate text or content and indexed this, rather like a library, for ease of access later by search engine users.

2. The computer model assigns a confidence score to the algorithm variable for how accurate a judgment it is making for that stage of a business decision-making process.

3. Where ‘computer confidence’ is below the software designer’s specified value, the decision and associated data will be allocated to a human annotator for their judgment.

4. Any human assessment is used both for performing that particular business process the algorithm was designed for and is also inputted to the machine learning algorithm to make it smarter and potentially automate this process in the future i.e. the machine learns as it interacts with humans.

The human in the loop data processing procedure is now in use in many well-known businesses, from Google’s web page indexing and reviews to Pinterest’s process for passing posts for display according to their publication policy.

Why is it important to keep the loop

Biewald offers insight into why HILC is significant: “I’ve worked with many companies building machine learning algorithms and I’ve noticed a best practice in nearly every successful deployment of machine learning on tough business problems… called “human-in-the-loop” computing.” (Emphasis added)

Of course, Biewald’s commentary could be viewed with some skepticism, as he has an interest in critiquing the accuracy of machine data learning, as only one essential element of software design given his company sells supplementary crowd-based services.

Nevertheless, his services and HITL are both vital for maximizing the strength of automated processes.

Biewald argues that AI models that don’t have some sort of human in the loop involvement are flawed. AI critics claim it’s only accurate to around 80%.

This is now a general acknowledgment by computer scientists and software developers that there’s still a need for humans to be involved in creating algorithms to automate business functions because of their capacity for integrating long-term quantitative and qualitative objectives. The combination of machine and human intervention in solving business challenges solves the problem of achieving maximum machine accuracy.

Organizations implementing automation, therefore, still need high-quality programmers and active involvement in key decision making from staff to formulate effective automation processes. We’ve found this balance is critical for long-term success.

Biewald has commented, that computers perform well in the analysis of difficult tactical situations, but still have limits in understanding long-term strategy, a task humans still excel at compared to AI.

Many software developers now operate the Pareto 80-20 formula in business process design, whereby human beings manage 20% of machine learning algorithm design, given that achieving 80% accuracy in real-world applications poses potentially life-threatening risks e.g. self-drive vehicles.

In an article (as of 2016) on how artificial intelligence (AI) is transforming the world of work, one tech commentator states: “…the real challenge goes well beyond merely accessing more data. The key is accessing data in the right way, at the right time, and in the right format to generate beneficial insights.”

Data, humans and machine learning for the future of business

Machine learning including the human in the loop has become mainstream, not only for the bigger players, such as Google and Pinterest.

As more SMEs seek to save time and staffing costs, automation has become a cost-effective necessity for many businesses. Technical innovation is what will help companies gain their edge for the future. That’s the key.

Technology represents about 9% of our conversations at Tallyfy, and understanding and actively monitoring one’s own business metrics will probably ensure that your software continues to perform as your business inevitably changes and grows over time.

Ensuring consistently ‘active learning’ of technology (or semi-supervised machine learning) where a computer program’s learning algorithm periodically and interactively asks questions of a user (or user group) is likely to maximize validity and relevance.

Business owners seeking to undertake software development and automation projects will benefit from ensuring that they find a partner who is keen to ensure that your technological solutions will continue to serve your business regardless of new future developments.

HITL programming will gather your desired data for business outputs and ensure data points stay up to date, so your essential users can continue to interact with your systems to not only make the best decisions for your company based on available data but play their part in being the necessary human in the loop of consistency and continual business improvement.

Related questions

What is the difference between human-in-the-loop and human-on-the-loop

Human-in-the-loop refers to a situation where a person is actively making decisions and taking actions in a process, such as a pilot flying a plane vs. autopilot. Human-on-the-loop refers to human oversight of an automated system, with humans intervening only when necessary, as when a pilot monitors an autopilot. It’s kind of like driving a car (in-the-loop) vs watching a self-driving car (on-the-loop).

What is the acronym for human-in-the-loop

The abbreviation for human-in-the-loopis HITL. That shorthand is often used in automation, AI, and workflow debates to refer to systems in which humans are involved in decision making.

Why is human-in-the-loop important in automation

Human-in-the-loop ensures humans remain at the core of automated systems - human intelligence combined with machine speed and accuracy. It’s there to catch mistakes, to make exceptions, to be a judge in ways machines can’t. Think of a spam filter that picks up on your selections of what you consider important versus what you believe is junk.

What are examples of human-in-the-loop systems

This is even common for systems in healthcare: when a doctor agrees or disagrees with the diagnosis from the medical system, she is essentially being the machine’s label. This estimation is also performed by the customer service chatbot transferring a complex issue to a human. Even smart home products rely on HITL when they ask you to validate or correct their automated decisions.

How does human-in-the-loop improve artificial intelligence

With humans in the loop, AI systems learn and improve based on feedback, mistake corrections and decision validations.

It’s akin to teaching a child - the AI proposes an answer, and humans guide the machine toward what’s right or wrong, thereby instructing it a little bit each time.

What industries benefit most from human-in-the-loop

Healthcare, financial services, legal and manufacturing industries receive significant benefits from HITL. These are the fields that rely on both automated efficiency and human judgment to make important decisions that affect people’s lives, money and safety.

What are the challenges of implementing human-in-the-loop systems

The lead challenges are determining the right level of automation and human involvement, keeping humans engaged when most of the tasks are automated and creating interfaces that make it easy for humans to step in when necessary.

It’s like choreographing a dance where both dancers have to know the step, and both have to know when to lead, and when to follow.

How does human-in-the-loop affect workflow efficiency

Pure automation might feel faster, but frequently HITL achieves better outcomes by catching errors and enhancing decision quality. Think of it as a slightly longer route that is usually more predictable compared with a shorter route that could have stop signs along the way.

What is the future of human-in-the-loop automation

The future could see more advanced cooperation between man and machine like AI doing the routine work while man handles the more intricate parts that require complex decisions and creative problem solving.

It’s playing out as a genuine partnership in which each partner brings to bear its inherent strengths.

How do you determine when human-in-the-loop is necessary

Consider HITL for high-stakes decisions, emotional intelligence decisions, creativity decisions or any decisions in unfamiliar territory. For example, a bank could automate routine transfers but require human input for large, unusual transactions.