Author(s),: SupriyaGhosh Machine Learning The integration of Human-in the-Loop in machine learning (HITL) is a requirement, and not a choice. Here’s why? Source: Andy Kelly, Unsplash. To fully integrate Human-in the-Loop in Machine Learning (HITL), it is important to understand HITL and its benefits and methods. What does Human-in the-Loop (HITL), mean? What is Human-in the-Loop? (HITL)? It’s the process of integrating the inputs from humans and their feedback into the machine learning architecture. It starts with integrating the human-labeled/annotated data into the machine learning model and goes through a feedback cycle to train models to yield the desired output through the support of humans continuously. Photo by author. Feedback allows for improvement in the prediction model if it falls below a level of confidence. It is wise to establish the acceptable level of confidence for your model before you start using it. The threshold could be somewhat lower if there are few incorrect predictions. This will require less manual intervention. However, high accuracy predictions require more human intervention (manual). The Human-in-the-Loop approach (HITL), combines both human and machine intelligence. What is the HITL approach to Machine Learning? The machines are great at making intelligent decisions from large datasets. However, people can make sound decisions with limited information. Therefore, the design must include both machine’s smart computational decisions as well as human right/ethical decision making. Human-in-the-loop(HITL) in machine learning are designed in such a way that allows both sides(machines and humans) to interact continuously and includes a continuous feedback loop which enables feedback from the training, parameter tuning and testing tasks to be fed back into the algorithm so that it gets smarter, more confident, and more accurate. Image by the Author. This makes it particularly effective because the algorithm decides what to learn next, and then sends that data to humans to label and train. Active Learning is a process that improves the machine’s learning. Active learning is the method of making sure that the model gains something from every new labeled/annotated point of data. This allows it to continue moving forward in its process. Active learning is one of the most important aspect of machine learning models and when combined with Human-in-the-loop(HITL) delivers optimal results. A few examples of this are: Smart devices become smarter when they hear more speech signals and command. By observing what websites users click on for a specific search term, search engines become more efficient. With more humans riding and drivers, autonomous vehicles become smarter. Machine Learning apps can get HITL benefits. 1. Data can often be incomplete or unambiguous. To provide context for machine learning models to learn, humans annotate/label the raw data. This allows them to identify patterns and take correct decisions. 2. The models are checked by humans for excess fitting. The model is taught about unusual situations and extreme cases. 3. To make correct decisions, humans evaluate whether the algorithm is confident or too optimistic. The machine will go through active learning if it is not accurate. This cycle allows humans to provide feedback that helps the machine reach its desired result. 4. This allows for a substantial increase in transparency, as the application is no longer seen as a Black box without humans being involved at every stage. 5. This combines human judgement in the best ways, shifting pressure from creating “100% perfect algorithms to optim models that offer maximum business benefits. It allows for more useful and powerful applications. How can Machine Learning maximize its benefits by using HITL strategies? 1. All people involved with the data, such as those who train and collect the data, are included. 2. To improve models and maximize their results, strategically deploy people at all stages of the process. 3. Employ qualified and well-managed human resources that can accelerate the time to market, and decrease the data processing load. 4. If possible, induct the resources before the model development starts. Machine learning models can be obsolete sooner than other software systems due to the fact that they rely heavily on constantly changing data. 5. Poor utilization throughout the entire lifecycle is a sign of poor data quality, high costs and even model failure. 6. Although full automation should not be your ultimate goal, sometimes it is possible to achieve the greatest results only with human (those who have the necessary knowledge and expertise) or machine cooperation and coordination. 7. Automating should balance tasks that are useful and those where humans can still participate. It is not a win-win scenario to try to automate everything and rush to automate all of it. Ideal solutions are often found somewhere in the middle. 8. If there are ethical decisions to be made, such as when a child is being raised, it should be allowed. Human empowerment should prevail in situations where the right answer is not clear or machine failure is possible. 9. With proper risk assessment, context-dependent decision-making systems that are human-led should be used to solve complicated environmental management issues. In such situations, human checkpoints must not be ignored. 10. 10 Someone must be accountable for decisions made by ML or AI applications. This cannot be automated. Because the risks and stakes are too great for one wrong turn, it must be human. Human-to-machine involvement ratio as determined by scientists and researchers. Image: Author. Despite the fact that Machine Learning is used in many different areas and organisations, the Human-in the-Loop approach (HITL), hasn’t been fully utilized. This is still a relatively new area and must be considered carefully in order to integrate with machine learning applications. The good news? Most industry professionals now recognize its importance and are preparing to integrate HITL into every AI and ML application. Thank you for reading! Follow me on LinkedIn and Medium: SupriyaGhosh. Twitter: @isupriyaghosh. Integrating human-in-the loop (HITL), in machine learning applications is not an option but a necessity. This story was first published on Medium by . People are responding and highlighting it. Published via
