As the world embraces artificial intelligence and machine learning, the importance of incorporating human expertise into the development and optimization of these models cannot be overstated.
Enter Human-in-the-Loop Machine Learning (HITL ML)—a methodology that combines the power of advanced algorithms with the critical thinking and domain knowledge of human experts. This approach has proven successful in a wide range of industries, from healthcare and cybersecurity to natural language processing and transport.
The key to successful HITL ML lies in leveraging human expertise at various stages of model development. This collaborative process improves the accuracy and efficiency of AI models, can address potential biases and ethical concerns, and allow for ongoing refinements. However, implementing HITL ML has its challenges—like finding experts with the necessary skills and domain knowledge.
In this blog post, we'll look at the role of human experts in HITL ML, the benefits and challenges of the approach, and how Akkio's predictive AI platform can help you implement HITL ML in your organization.
HITL combines the power of automated ML with human expertise to achieve more accurate data processing and analysis. What's more, HITL allows organizations to harness the strengths of both humans and machines, resulting in more effective and efficient problem-solving.
And the benefits of incorporating a human-in-the-loop to ML don't end there:
HITL can play an essential role in both supervised and unsupervised learning. In supervised learning, the algorithm is trained on labeled data, where the correct output or target variable is known for each input. The key inputs of humans involve identifying problems with the models and data (like missing data) and working alongside the model whenever the predictions are potentially of worse quality.
On the other hand, unsupervised learning is an ML approach where the algorithm is trained on unlabeled data without any predefined output or target variable. The algorithm's goal is to discover patterns, structures, or relationships in the data, by clustering similar data points together or reducing the dimensionality of the data without any prior knowledge or guidance.
Then, humans are tasked with transforming the patterns discovered by the model into information relevant to the business (e.g., one cluster contains users more likely to stay after a free trial expires, and another cluster is users more likely to avoid paying).
ML algorithms are incredibly powerful, particularly when paired with HITL strategies, but human input shouldn't be overlooked. Unlike standalone algorithms, HITL acknowledges the cognitive abilities of humans in comprehending complex or abstract concepts and handling ambiguous scenarios. As a result, the ML model can learn from human insights and improve their overall performance.
So, let's explore the role of experts in HITL ML:
Supervised learning algorithms need data to learn from. However, while data labeling can be expensive and time-consuming, HITL speeds up the process by leveraging human expertise to label data. For instance, a human expert can label a small subset of the data, and the algorithm can learn from these labeled examples to predict the labels of the remaining data. Then, the human expert can review and correct the predictions made by the algorithm and facilitate a more accurate and efficient labeling process.
There's also a family of ML approaches known as active learning, where the model identifies which examples are most likely to help the algorithm learn. These methods can be implemented with a human in the loop to achieve optimal labeling—and active learning is recommended over manual human examinations of labels and predictions.
Supervised learning requires that algorithms are trained on a labeled dataset and that the model's performance is evaluated on a separate validation set. HITL helps by having a human expert review the model's output to provide feedback on mistakes. Then, the expert can use this insight to adjust model parameters and improve its performance.
Supervised learning models can be complex, making it difficult to interpret how the model makes its predictions. Fortunately, HITL can provide a solution—tasking the model with generating a confidence score based on its decision-making and having a human expert review the examples where the model isn't entirely sure of the outcome.
Having a confidence score alongside the ML model's predictions captures how likely predictions are to be correct. This makes it possible to separate predictions into "trivial, no human intervention necessary until QA" and "the model isn't sure; a human should probably look into this." By doing so, a business can release humans from routinary cases – and make their jobs more enjoyable—while avoiding catastrophic failure should there be any previously unseen data.
It can be tricky to determine the optimal number of clusters to use. Incorporating HITL allows humans to interact with the clustering algorithm and provide feedback on the optimal amount of clusters for a given context.
There are also rules that help determine the optimal number of clusters (like the elbow rule and information-theoretical bounds), but these tend to be quite application-specific. So, it's better to have a human look at the outputs and apply their background/domain expertise.
Unsupervised learning can be prone to overfitting, where the model becomes too complex and begins to fit the noise in the data rather than the underlying patterns. While overfitting will be immediately diagnosed from the gap between training and validation loss, a human can determine how the model is overfitting, identify if the training/test split is suboptimal, and even pick out potential issues in the model architecture.
As unsupervised learning often involves discovering patterns or structures in the data, humans can verify and interpret these findings and help fine-tune learning models to provide more meaningful and actionable insights.
Remember that while algorithms can find patterns in data, only humans can interpret and transform them into a story capable of answering business-related questions.
HITL ML has utility across a huge number of industries, helping organizations achieve more accurate and reliable results. Here are just a few real-world examples of where this methodology works well:
When it comes to medical diagnoses, the stakes are high and errors can have serious consequences—which makes HITL ML especially useful. Human oversight also complements the model's outputs. For example, a simple BMI algorithm can label a patient as overweight, despite the fact they might just be tall, short, or particularly fit. To combat this, a clinician can use the algorithm for most medical cases while guiding the outliers.
In healthcare, HITL can help clinicians interpret data more efficiently, quickly identifying areas requiring examination and allowing experts to find extra information the algorithm may have missed. Then, human feedback on model predictions can help identify errors and ensure that the model remains accurate and reliable.
HITL ML excels at identifying transaction data patterns, detecting anomalies, and preventing fraudulent activity. By spotting and correcting errors, humans ensure that the model learns from its mistakes over time and becomes more and more effective. Moreover, humans can correct algorithmic biases and prevent potential discrimination by ML methods.
Humans are also good at spotting and responding to potential threats in real time, enhancing the overall security of systems and networks.
HITL can help improve the performance of natural language processing (NLP) models by involving human experts in tasks like sentiment analysis, machine translation, and text summarization. And here's where the concept of a confidence score comes in handy! Using a confidence score, humans can help process predictions and examples that are less likely to be correct, ensuring more accurate results and streamlining workflows.
HITL can be employed to improve the efficiency of content moderation systems on social media platforms and online communities by involving human moderators in identifying and labeling inappropriate or harmful content. By combining human expertise with machine learning algorithms, content moderation systems can become more effective in maintaining a safe and positive online environment for users.
Implementing a HITL approach in ML comes with its own unique set of challenges. Below, we've outlined two of the most common obstacles:
In a nutshell, human annotation refers to adding metadata or labels to data to make it more useful for machine learning algorithms. It's an integral part of the HITL ML process, as it helps train models and improve their accuracy. However, ensuring quality human annotation isn't a straightforward task:
The challenge of human annotation can be primarily overcome through active learning— an ML technique in which an algorithm actively selects the most informative examples from a pool of unlabeled data for annotation or labeling by a human expert.
Finding and retaining experts with the necessary skills and domain knowledge can be a daunting task. Organizations may need to invest in training or partnerships to scout out the right people with the right expertise. Other expertise-related challenges include:
Akkio is an ML platform that can be utilized across various industries, including healthcare, transportation, website design and development, marketing, sales, finance, and more. And, because Akkio is a no-code platform, you don't need to be a computer science wizard to maximize its ML training potential!
Akkio is accessible to a range of users and allows businesses to focus on their core competencies while leveraging the benefits of data analysis. Additionally, Chat Explore lets users dig into their data without a hassle, saving time and reducing the learning curve associated with traditional data analysis methods.
Thanks to a variety of integrations, Akkio can connect directly to your data and route predictions anywhere. This makes it especially easy for the human experts in your team to review predictions made by the ML model and see if they're accurate or if errors, biases, or other issues need to be corrected. So, if you want to implement ML methodology that includes HITL, Akkio can help you out.
With Akkio, you’re in control of:
Implementing HITL methodology in ML offers several benefits, ranging from improved accuracy to reliability and relevance of AI models. What's more, the combination of ML and human expertise works incredibly well in a vast range of industries, enabling businesses to make more informed, data-driven decisions.
Implementing HITL ML in your organization is easy with Akkio. The user-friendly, no-code platform ensures that a human is in control of the process from start to finish—and that they can observe the output and improve the model over time effortlessly.
So, if you want to experience the advantages of HITL ML for yourself, and drive better outcomes that can put your business one step ahead of the competition, try Akkio today and begin your journey towards more accurate and efficient AI models!