Top 5 Common Training Data Errors and How to Avoid Them

Avoid training data errors

In traditional software development, the code is the most critical part. In contrast, what’s crucial in artificial intelligence (AI) and machine learning (ML) development is the data. This is because AI training data models include multi-stage activities that smart algorithms must learn in order to successfully perform tasks .

In this scenario, a small mistake you make during training today can cause your data model to malfunction. This can also have disastrous consequences—for example, poor decisions in the healthcare sector, finance, and of course, self-driving cars.

So, what training data errors should we look out for, and what steps can you take to avoid them? Let’s look at the top five data errors and how we can prevent them.

1. Potential Labeling Errors

The most common error that appears concerns data labeling. According to a study conducted by researchers at MIT, databases used to train countless computer vision algorithms had an average of 3.4% errors across all datasets. While that might not sound like much, the quantities actually ranged from just over 2,900 errors to over five million errors.

As such, high-quality data sets are therefore essential for the development of powerful data training models. However, this isn’t always easy, as poor-quality data isn’t necessarily obvious. Data units typically contain files with audio snippets, images, texts, or videos.

For example, if you task data annotators with drawing boxes over images of motorcycles, they will draw bounding boxes around all photos of motorcycles. The intended outcome is tight bounding boxes around motorcycles. The label assigned to the file, or the file attributes gives the file meaning. Label attributes must include the time it was labeled, who labeled it, and under what conditions.

Sometimes, you might miss some labels as the annotator didn’t place a bounding box around all the motorcycles in an image. Or it could be a misrepresentation of instructions where the annotator does more than what’s required. Or it could be something as simple as an incorrect fit.

How do I avoid such errors?

We can mitigate the risk of making such mistakes by providing annotators with clear instructions to avoid such cases.

2. Testing Models with Used Data

It isn’t wise to reuse data to test a new training model. Think of it this way: if someone already learned something from the data and applied it to an area of their work, using the same data in a different area might lead to mistakes and bias. You also increase your risk of exposure to repetitive inferencing.

Like in life, ML follows the same logic. Intelligent algorithms can predict answers accurately after learning from a bulk of training datasets. When you use the same training data for another model or AI-based application, you might end up with results that relate to the previous learning exercise.

How do I avoid such errors?

To avoid any potential bias, you must go through all the training data to determine if any other projects had used the same data. It’s crucial to always test data models with new datasets before embarking on an ML data training exercise.

3. Using Unbalanced Training Datasets

You must carefully consider the composition of your training datasets as data imbalance will likely lead to bias in model performance.


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Datasets for Machine Learning

When it comes to unbalanced datasets, you have to look out for two types of errors:

  1. Class imbalance often occurs when you don’t have a representative dataset. For example, if you’re training your algorithm to recognize males, but your training data model only represents one ethnicity your model will only perform well at identifying males of the displayed ethnicity represented in the training model. In this case, ML algorithms may miss all other ethnic groups.


  2. Data recency matters as all models degrade over time as the world evolves and moves forward. For example, after the onset of the pandemic, recognizing human faces became increasingly challenging with the addition of facemasks and PPE equipment.

How do I avoid such errors?

Always make sure that your training datasets are highly representative and recent.

4. Using Unstructured, Inefficient, or Unreliable Training Datasets

Building reliable ML models depends entirely on your datasets. In this scenario, you should consistently update training data that’s recent and representative. This approach will alert you to potential flaws in the system’s decision-making process.

However, enterprises often underestimate the importance of following best practices and end up wasting time and resources with inaccurate or biased unusable data. In this case, it could also lead to project failure and long-term losses.

How do I avoid such errors?

Even if your organization has tons of petabytes of unique data, it’s critical to use only data that’s relevant, cleaned, and processed for your data training project. Companies can ensure that they only use relevant data for AI training purposes by taking a data-first approach.

This approach will also help you better understand outputs and identify potential inaccuracies and biased outcomes.

5. Potential Bias in Labeling the Process

If you’ve read this far, you’ll know that bias is something that keeps coming up time and time again. The risk is always ever-present whether there is bias in the labeling process or bias because of annotators. In the same vein, you can also cause bias when the data demands specific knowledge or context.

For example, if you’re using data from around the world, you might have training data errors because of mistakes made by the annotators: If you’re working with British annotators, they will classify a sidewalk as a “pavement.” Or, if you’re trying to identify different types of food, American annotators will struggle to identify dishes like “the Haggis”, a savory pudding made with a sheep’s heart, liver, and stomach, which is also the national dish of Scotland.

How do I avoid such errors?

In the scenario described above, you’ll find a bias towards a nation-specific mindset. This makes it critical to involve annotators from around the world to ensure that you’re capturing accurate information.

Successful AI projects depend heavily on fresh, accurate, and unbiased representative data to mitigate agency and moral risks. This makes it vital for businesses to implement quality checks throughout the data labeling process and testing exercises. This approach will help you identify and resolve potential errors before they become a problem.

The good news is that you can simply do this by using AI-powered double-check annotators for intelligent labeling. You must also have humans within the loop to monitor the model performance to eliminate bias.

Whenever reducing bias is essential, you have to recruit a diverse group of annotators from around the globe with the necessary domain knowledge demanded by your project. You can quickly achieve this without any of the HR headaches or overheads that go with it by leveraging crowd worker communities.



Andrew Zola