Imagine you’re working on an ambitious AI project poised to revolutionize the industry, yet you find yourself bogged down by the tedious, time-consuming task of data labelling. Sounds familiar? For many, this is a common dilemma. Collecting and labelling data is often the most expensive and painstaking part of AI projects, especially when fresh data is continuously required.

What is Model-Assisted Labelling?

Model-assisted labelling can be a game-changer. At its core, this strategy involves training an AI model concurrently with the manual labelling process. As the AI begins to learn and recognize patterns in the data, it starts suggesting labels, significantly speeding up the labelling process by allowing human workers to approve pre-suggested labels with a single click.

This approach can be implemented by training a model specifically for labelling purposes or by incorporating the actual production model into the labelling loop.

Why Consider Model-Assisted Labelling?

The Pros

• Speed and Efficiency: Human labellers can work much faster by approving suggested labels instead of manually selecting them each time. This efficiency is particularly beneficial when working with large datasets or documents with multiple potential labels.

• Early Insight into Model Weaknesses: Using model-assisted labelling provides early, hands-on insight into the model’s weak points. Identifying which instances are difficult for the model to understand allows teams to address these issues early, thereby improving overall model performance.

Example: Imagine working with a customer service AI that needs to categorize customer complaints. Model-assisted labelling could quickly highlight that the AI struggles with certain complaint types, prompting targeted data collection to enhance its accuracy in those areas.

The Cons

• Potential for Lower Data Quality: Humans tend to prefer defaults, and when they go on autopilot, they may accept incorrect suggested labels. This tendency can lead to a drop in data quality, which might result in a poorly performing model.

• Pre-Labelling Quality Concerns: If the AI model’s pre-labelling quality is low, correcting these errors may take more time than manual labelling from scratch. Starting with a blank slate could sometimes be more efficient.

Practical Tips for Successful Model-Assisted Labelling

• Set a Data Quality Target: Accept that achieving 100% correct data is unrealistic. Establish a data quality threshold that is acceptable for training the model. This benchmark helps monitor whether the model-assisted labelling process is beneficial or detrimental.

• Include Non-Pre-Labelling Samples: Periodically disable the assist model to measure the quality difference between pre-labelled and non-pre-labelled data. This can be as simple as turning off the assist model for one out of every ten cases, offering valuable insights.

• Utilize Probabilistic Programming Models: Bayesian probabilistic models provide uncertainty in distributions instead of scalar values, making it easier to determine the likelihood of a correct pre-label. This approach can enhance the effectiveness of model-assisted labelling.

Final Thoughts

Model-assisted labelling holds great promise for streamlining AI projects and reducing costs. However, careful management is crucial to avoid pitfalls that could compromise data quality and model performance. By setting clear targets, continuously evaluating the process, and leveraging advanced models, you can harness the power of model-assisted labelling to supercharge your AI projects.

What strategies have you found effective in improving data labelling processes? Share your insights and let’s continue the conversation!