Why human-in-the-loop is a must in high-quality ML data annotation
Machine learning (ML) is altering how we use technology. Increased use of ML and AI has also made systems vulnerable to shortcomings of AI and ML models. Inferior quality of data annotation is one of the biggest issues that cause ML and AI to perform poorly.
However, high-quality data annotation is not possible without human supervision and feedback, called human-in-the-loop, or HITL. This is why, in this article, we’ll take a look at the role and benefits of incorporating human-in-the-loop (HITL) in data annotation for machine learning and why it is the future of machine learning.
HITL can overcome the drawbacks of fully automated ML and pave the way for more precise, dependable, and ethical AI systems. It works by fusing the advantages of automated algorithms with human skill, intuition, and decision-making.
The limitations of fully automated machine learning
Datasets with insufficient or faulty labels negatively affect ML model training and performance. Unsupervised automation of annotation may help save time and costs, but ignoring the need for human judgment in complex subjects seriously influences ML project outcomes.
Although fully automated data annotation looks efficient and scalable, especially in the creation of ML training datasets, it cannot be relied upon except in simplistic cases. Because automated replication of errors takes place. Fully automated systems have a higher propensity for error and bias, contextual imprecision, lack of domain competence, and inability to manage complicated data types than a setup incorporating HITL.
Some pitfalls of fully automated annotation mechanisms
Here are a few actual instances where using entirely automated data annotation led to mistakes, further proving the need for HITL in precise annotations.
- Image recognition misclassifications — Google Photos came under fire in 2015 after their automatic image recognition technology incorrectly identified images of African-American people as “gorillas.” This goof-up brought to light the inaccuracies of an automated system’s ability to recognize and categorize photos, especially when it comes to managing a variety of human beings.
- Biased sentiment analysis — Studies have established that automated sentiment analysis algorithms display biases depending on things like ethnicity, gender, and cultural background. Sentiment analysis algorithms trained on social media data were found to display biases by associating particular racial and ethnic groupings with more unfavourable attitudes.
- Autonomous vehicle systems — Fully automated systems have the potential to misread or misclassify road situations in the context of autonomous driving, creating significant risks. In several cases, the failure of autonomous vehicles to appropriately recognize specific objects or conditions led to accidents or close calls.
- Adversarial attacks on computer vision systems — Fully automated computer vision systems have been successfully tricked by adversarial assaults. For instance, researchers have shown how minute changes to photos, which are frequently invisible to people, can lead automated systems to completely misclassify objects or produce inaccurate annotations.
- Inaccurate transcription in speech recognition — Fully automated speech recognition systems may have trouble reliably transcribing spoken words, especially in situations where accents, dialects, or other speech variances are present. Inaccuracies in the transcriptions and subsequent annotations may result from this.
To avoid these issues and ensure more accurate and trustworthy annotations incorporate human intervention, contextual knowledge, and quality control techniques.
Advantages of human-in-the-loop in machine learning
By incorporating human intelligence into the machine learning pipeline, the “human-in-the-loop” setup enables people to actively influence the learning process. Human specialists have domain expertise, insight, and decision-making skills that automated algorithms do not have. HITL can supplement and improve automated algorithms by utilizing human expertise, resulting in increased model accuracy, adaptability, and transparency.
HITL has several benefits over completely automated methods.
- First of all, involving a person in the process improves data labeling and annotation, resulting in high-quality labeled datasets that form the basis of reliable ML models.
- Humans are capable of making nuanced decisions and can manage edge cases and complex situations that automated systems could find challenging.
- The flexibility and adaptability of HITL also enable repeated model iteration and modification depending on user feedback.
- The overall performance and dependability of ML models are improved by this iterative procedure.
Additionally, the human-in-the-loop approach is essential for building confidence and responsibility in AI systems. Transparency, bias mitigation, and fairness can all be addressed by including people in the annotation process.
Techniques within the HITL framework for high-quality annotation
Human-in-the-loop (HITL) approaches are specifically designed to tackle the limitations of fully automated data annotation by incorporating human intervention. Here are some techniques within the HITL framework that can help counter these limitations:
Iterative annotation — Initially, a small subset of data is annotated by human annotators. The automated system then learns from these annotations and makes predictions on the remaining data. Human annotators review and correct the automated annotations, and this revised dataset is used to improve the model. The iterative process continues, with the model gradually becoming more accurate over time.
Uncertainty sampling — The automated system can identify data points where it is uncertain or has low confidence in its predictions. These uncertain samples are then prioritized for human annotation. By focusing human effort on challenging cases, the system can improve its performance more efficiently, ensuring accurate annotations in areas where automation may struggle.
Active learning — In active learning techniques, samples are intelligently chosen by the automated system for human annotation. The technology selects the most instructive or challenging-to-classify data items and delivers them to human annotators. This approach optimizes the use of human resources, as annotators focus on the instances that are most beneficial for improving the model’s performance.
Review and correction — Human annotators review and correct the automated annotations, ensuring accuracy and contextually appropriate annotations. This review procedure aids in improving the model’s comprehension of the data and correcting any biases or inaccuracies brought about by automation.
Quality control and feedback — In the HITL approach, establishing quality control systems is essential. Annotators can offer input on the system’s functionality and any inconsistencies they come across while annotating. This feedback loop allows for continuous improvement and refinement of the automated system, addressing any limitations or issues that arise.
Expert guidance — Domain specialists are essential in HITL for directing the annotation process. They give clarifications, clear out misunderstandings, and ensure that the annotations adhere to the particular needs of the domain. Their experience improves the accuracy of the annotations and the contextual comprehension of the data.
Inter-annotator agreement — Introducing multiple annotators for the same data points and calculating inter-annotator agreement helps measure the consistency and accuracy of the annotations. Discrepancies among annotators can be addressed through discussions, training, or clarifications, leading to improved accuracy, and reducing potential biases.
Real-world applications of HITL in data annotation
All critical real-world applications showcase the successful implementation of HITL in data annotation. For instance, in medical imaging, HITL enables accurate and reliable annotation of medical images, leading to improved diagnosis and treatment. In autonomous driving, HITL is crucial for annotating complex traffic scenarios and enhancing the safety of self-driving vehicles. These applications highlight the significant impact of HITL on data quality, model performance, and overall accuracy.
Also read: Image Annotation: A Complete Guide with Examples
The future of machine learning: HITL as a standard practice
The use of HITL in the AI/ML sector is anticipated to rise as machine learning technology develops further. To overcome the drawbacks of entirely automated ML data annotation, combining automated algorithms with human intelligence will become standard practice.
Additionally, improvements and innovations in HITL tools and processes will increase its effectiveness and efficiency. AI/ML platforms and enterprises may fully use machine learning and create more reliable, responsible, and trustworthy AI systems by embracing HITL.
The future of machine learning is a collaborative one, with the importance of the “human in the loop” being paramount. We may anticipate more advanced tools and methods that enable people to successfully offer their expertise as technology develops. Higher levels of performance and accuracy in machine learning will be attained with the use of “human-in-the-loop” techniques.
Conclusion
HITL overcomes the constraints of fully automated ML and improves the quality, accuracy, and fairness of AI systems by utilizing human intellect, intuition, and decision-making. It offers many benefits, including enhanced data labeling, handling complicated circumstances, adaptability, and transparency. Insufficient or incorrect labels, confusing annotation tasks, scaling efforts, bias in annotations, and changing requirements are all issues that are addressed.
Applications in the real world outside data labs show how HITL is successfully implemented and how it affects model performance and data quality. The adoption of HITL as a best practice will ensure trustworthy and accountable AI systems that benefit society as a whole as the AI/ML sector develops.
However, implementing human-in-the-loop in machine learning can pose challenges such as striking the right balance between automation and human intervention, ensuring scalability, and maintaining annotation consistency. Overcoming these challenges requires careful planning, iterative feedback loops, and the use of active learning techniques. Outsourcing such tasks to experts in the field can prove beneficial for both your project and your organization.
