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?
Biewald argues that AI models that don’t 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
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 programme 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:
- 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.
- 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.
- 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.
- 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 maximising 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 that it is only accurate to around 80%. This is now a general acknowledgment by computer scientists and software developers that there is 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.
Software project clients, therefore, still need high-quality programmers and active involvement in key decision making the staff to formulate effective automation processes. 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 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 is undeniably becoming mainstream, not only for the bigger players, such as Google and Pinterest, however. As more SME’s seek to save time and staffing costs, automation is becoming a cost-effective necessity for many businesses and technical innovation is what will help companies gain their edge for the future.
It may be that understanding and actively monitoring one’s own business metrics will 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 ask questions of a user (or user group) is likely to maximise 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 means a person actively makes decisions and takes actions within a process, like a pilot flying a plane. Human-on-the-loop means someone supervises an automated system but only steps in when needed, like a pilot monitoring autopilot. Think of it as the difference between driving a car (in-the-loop) versus watching a self-driving car (on-the-loop).
What is the acronym for human-in-the-loop?
The acronym for human-in-the-loop is HITL. This shorthand is commonly used in automation, artificial intelligence, and workflow discussions to describe systems where humans play an active role in decision-making.
Why is human-in-the-loop important in automation?
Human-in-the-loop keeps people at the heart of automated processes, combining human intelligence with machine efficiency. It helps catch errors, handle exceptions, and make judgment calls that machines can’t. Imagine a spam filter that learns from your choices about which emails are actually important versus junk.
What are examples of human-in-the-loop systems?
Common examples include content moderation on social media, medical diagnosis systems where doctors confirm AI suggestions, and customer service chatbots that transfer complex issues to human agents. Even smart home devices use HITL when they ask you to confirm or adjust their automated decisions.
How does human-in-the-loop improve artificial intelligence?
Humans help AI systems learn and improve by providing feedback, correcting mistakes, and validating decisions. It’s like teaching a child – the AI suggests an answer, and humans help it understand if it’s right or wrong, helping it learn from each interaction.
What industries benefit most from human-in-the-loop?
Healthcare, financial services, legal, and manufacturing industries see huge benefits from HITL. These fields need both automated efficiency and human judgment for critical decisions that affect people’s lives, money, and safety.
What are the challenges of implementing human-in-the-loop systems?
The main challenges include finding the right balance between automation and human involvement, maintaining human engagement when most tasks are automated, and designing interfaces that make it easy for people to step in when needed. It’s like creating a dance where both partners need to know exactly when to lead or follow.
How does human-in-the-loop affect workflow efficiency?
While pure automation might seem faster, HITL often leads to better overall results by preventing errors and improving decision quality. Think of it as taking a slightly longer route that’s more reliable versus a shorter route with potential roadblocks.
What’s the future of human-in-the-loop automation?
The future points toward more sophisticated collaboration between humans and machines, where AI handles routine tasks while humans focus on complex decisions and creative problem-solving. It’s evolving toward a true partnership where each side contributes its unique strengths.
How do you determine when human-in-the-loop is necessary?
Consider using HITL when decisions have significant consequences, require emotional intelligence, need creative thinking, or involve unusual situations. For example, a bank might automate routine transfers but need human review for unusually large transactions.
