In traditional software development, the efficiency of the delivered product depends on its code quality. The same principle applies to Artificial Intelligence (AI) and Machine Learning (ML) projects. The quality of the data model output is dependent on the quality of its data labels.
Poorly labeled data leads to poor quality of data models. Why does this matter so much? Low-quality AI and ML models can lead to:
- An adverse impact on SEO and organic traffic (for product websites)
- An increase in customer churn
- Unethical errors or misrepresentations
As data annotation (or labeling) is a continuous process, AI and ML models need continuous training to achieve accurate results. This requires data-driven organizations to avoid committing crucial mistakes in the annotation process.
Here are six of the most common mistakes to avoid in data annotation projects:
1. Assuming the Labeling Schema Will Not Change
A common mistake among data annotators is to design the labeling schema (in new projects) and assume that it will not change. As ML projects mature over time, data labeling schemas evolve and change over time. For example, labeling schemas can change in response to new products (or categories).
Data annotation is expensive when performed before the labeling schema is mature and finalized. To avoid this mistake, data labelers must work closely with domain experts (working on the business problem to solve) and have multiple iterations to stabilize the schema. Programmatic labeling is another effective technique that can prevent unnecessary work and wastage.
2. Insufficient Data Collection for the Project
Data is essential to the success of any AI or ML project. For an accurate output, annotators must feed their projects with large volumes of high-quality data. Further, they must keep feeding quality data to ML models to understand and interpret the information. One common mistake in annotation projects is collecting insufficient data for the not-so-common variables.
For instance, AI models are inadequately trained when annotators label their images for only commonly used variables. Deep learning data models need an ample quantity of high-quality data pieces. Hence, organizations must overcome the high cost of proper data collection, which can sometimes be impossible.
3. Misinterpreting the Instructions
Data annotators or labelers need clear instructions from their project managers on what they should annotate (or which objects to label). With misinterpreted instructions, annotators cannot create an accurate data model.
Here is an example: Labelers need to annotate a single object (using a bounded box). However, they may "misinterpret" the delivered instructions and end up "bounding" multiple objects in the image.
To avoid this mistake, project managers must articulate clear and exhaustive instructions which annotators cannot misunderstand. Additionally, data annotators must double-check the provided instructions to understand their work clearly.
4. Bias in Class Names
This mistake is also related to the previous point of misinterpreting the instructions (especially when working with external annotators). Typically, external labelers are not involved in schema designing. Hence, they need proper instructions on how to label the data.
Wrong instructions can lead to common mistakes such as:
- Priming the user to pick one product category over another.
- Adding bias in annotation projects in the form of data labels or suggestions.
- Using "biased" class names like "Others," "Accessories," or "Miscellaneous."
To avoid the common bias mistake, domain experts must have multiple interactions with the annotators, provide them with ample examples, and request their feedback.
5. Selecting the Wrong Data Labeling Tools
Due to the importance of data annotation, there is a growing global market for annotation tools, which is expected to grow at a healthy rate till 2027. Organizations need to select the right tools to perform their data annotation. However, many organizations prefer to develop in-house labeling tools. Besides being expensive, in-house labeling tools are unable to keep pace with the growing complexity of annotation projects.
Additionally, current annotation tools were developed in the earlier years of data analysis. They cannot handle Big Data volumes (and complex requirements) and lack the basic features of modern tools. To avoid this mistake, companies must look to invest in annotation tools developed by third-party data specialists.
6. Missing Labels
Data annotators often fail to label crucial objects in AI or ML projects. This can severely impact its quality. Human annotators can commit this mistake when they are not observant or simply miss some vital details. Missing labels are tedious and time-consuming to resolve for organizations, thus creating project delays and escalating project costs.
To prevent this mistake, annotation projects must have a clear feedback system communicated to the annotators. Project managers must set up a proper review process, where annotation work is peer reviewed before the final approval. Additionally, organizations must hire experienced annotators with soft skills like an eye for detail and high patience levels.
Conclusion
Accurate data labeling or annotation is a vital cog in AI or ML projects and can influence its output. The above-mentioned common mistakes can undermine the data quality, making it challenging to generate accurate results. Data-dependent companies can avoid these common mistakes by outsourcing their annotation work to third-party professional companies.
At EnFuse Solutions, we offer specialized data annotation services so that our customers can maximize their investments in AI and ML technologies. We customize our annotation services to each client's specific needs. Let's collaborate for your next AI or ML project. Connect with us here.
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