With AI becoming a critical aspect of businesses and over 77% of devices worldwide using it in one form or the other, the global AI market will reach $90 billion by 2025. Another study suggests that 80% of businesses will need AI and machine learning operations by next year.
The surging adoption of AI/ML models is mainly due to the efficiencies they offer businesses, yet they still rely on human intelligence and input for training. The data fed into AI models dictates their accuracy, and it is important to recognize that human involvement is indispensable throughout the process. Whether setting goals, designing algorithms, or ensuring that the model gets high-quality data, human intervention plays a critical role at every stage of AI development to its commercialization.
At iMerit, we strongly believe in the human-in-the-loop model for ML Data pipelines, and insights from our recent study in partnership with VentureBeat reinforce it. The study focuses on the challenges and outlook of industry leaders, data scientists, and tech professionals across major industries while building AI products into the market.
View the 2023 State of MLOps Report
This blog discusses why leveraging domain experts for data labeling and annotation is crucial for success with AI commercialization.
Why Data Labeling is Important for AI
AI algorithms rely on the data fed to make accurate predictions and decisions. To effectively deploy AI models in real-world scenarios, business stakeholders have to be confident about the predictions/ output the model is making. These predictions from the AI models are traced back to the annotation or labeling stage, and hence you need data labeling to be of high quality.
Improved labeling results in better data quality, leading to increased accuracy of the ML model in detecting, interpreting, and making precise predictions.
Key Stats Found:
- According to the research, well-labeled data significantly improves model performance, bumping it from an average of 60 – 70% accuracy to the 95% accuracy range.
- On average, 42% of all automated data labeling requires human correction or intervention.
- 86% call human labeling essential and currently leverage it at scale within their existing data labeling pipeline.
- 68% rely on a combination of automated and human labeling because while automation offers speed, humans are indispensable to validating results and identifying anomalies.
Need for Human Data Labeling
Manual/Human Data labeling can be time-consuming and expensive, often requiring a team of human annotators to label large amounts of data. However, despite its limitations, it remains an essential component of many machine learning applications.
Human Intelligence is Key for High-Quality Data Labeling
Higher Labeling Accuracy
Manual labeling helps to ensure a higher degree of accuracy and nuance in labeling, decreasing the chances of errors and misinterpretations. Data labeling experts with years of experience can understand the requirements of different machine-learning models and meet labeling demands with high accuracy rates.
Domain Expertise
To build the right data input for machine learning models, a comprehensive understanding of the domain and requirements is a must for annotators. For instance, data labeling in the healthcare sector can involve complex medical terminologies. Hence, in complex domains, it is advisable to have subject matter experts involved in the data annotation workflow to ensure the accuracy of data annotation and labeling.
Handling Edge Cases
Human Data labeling is critical when dealing with edge cases (unseen situations) or niche industries/sectors where public or synthetic datasets are insufficient or nonexistent. 82% of data scientists said data annotation requirements are becoming increasingly complex, and it is especially true as edge cases come to the forefront. Edge cases appear in response to the complexity and sheer variations in the real world, needing accurate representation in the input data.
Flexibility
As internal and external factors are prone to change, companies may require to modify the labeling guidelines or project requirements. Manual labeling allows for flexibility in the labeling process, allowing companies to make changes tuned to end users’ needs, product changes, or modifications in data models.
Quality Assurance
Quality assurance is an essential component of the data labeling process. For the machine learning model to work successfully, the labels on data need to reflect a ground truth level of accuracy, uniqueness, independence, and information. Humans can provide more accurate and meaningful insights than machines to ensure quality control.
Accountability
Humans can be held accountable for the quality of their annotations and can be trained to improve their performance. Data annotation tools, without any human intervention, cannot be held responsible for any biases, errors, or misrepresentations in the labeled data.
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Conclusion
We discussed the importance of data labeling and how human data labeling ensures high-quality data and is a key component for successfully deploying AI. A combination of automated and manual labeling gives organizations the speed, scalability, and accuracy needed for AI initiatives.
Check out iMerit’s 2023 State of MLOps Report for more such insights.
