Data bias is a sort of inaccuracy in machine learning that occurs when certain parts of a dataset are more strongly weighted and/or represented than others. A skewed dataset does not effectively represent the use case of a model, resulting in skewed results, low accuracy levels, and analytical mistakes.

Training data for machine learning projects should, in general, be representational of the actual world. This is significant since it is via this data that the machine learns how to execute its job. Data bias may arise in a variety of ways, including human reporting and selection bias, as well as computational and interpretation bias.

Types of Data bias

To address data bias in artificial intelligence technology, you must first identify where it exists. Only when you’ve identified a bias can you take the appropriate actions to correct it, whether that’s by addressing missing data or enhancing your annotation methods. With this in mind, it is critical to be careful about the scope, quality, and treatment of your data in order to eliminate bias whenever feasible. This has an impact not just on the accuracy of your model, but also on questions of ethics, fairness, and inclusiveness.

Though not complete, the following list provides frequent examples of data bias in the field, as well as examples of where it happens.

  • Measurement bias happens when training data varies from real-world data, or when erroneous measurements result in data distortion. A good example of this bias arises in image recognition datasets when the training data is taken with one type of camera while the production data is obtained with another. Measurement bias can also develop as a result of incorrect annotation during a project’s data labeling stage.
  • When the data for a machine learning model supports and/or magnifies a cultural prejudice, this is referred to as association bias. Your dataset may contain a set of jobs in which all males work as doctors and all women work as nurses. This isn’t to say that men can’t be physicians and women can’t be nurses. Female physicians and male nurses, on the other hand, do not exist in your machine learning model. Gender prejudice is well recognized as a result of association bias.
  • Confirmation bias is the consequence of only seeing what you anticipate or want to see in data. When researchers enter a project having subjective ideas about their work.
  • Recall bias is a type of measurement bias that is typical during the data labeling stage of a project. Recall bias occurs when comparable types of data are labeled inconsistently. As a result, accuracy suffers. Assume you have a team that labels photos of phones as damaged, slightly damaged, or undamaged. If one image is labeled as damaged while another is labeled as somewhat damaged, your data will be inconsistent.
  • Exclusion bias is particularly frequent during the data preparation step. Most of the time, it’s a matter of removing valuable material that was deemed irrelevant. It can, however, emerge as a result of the systematic exclusion of particular information.

Prevention of Data bias

Though it might be difficult to determine when your data or model is biased, there are some actions you can take to assist prevent bias or detect it early. Though far from exhaustive, the bullet items below give a starting point for thinking about data bias in machine learning initiatives.

  • To the best of your abilities, conduct a preliminary study about your users. Keep an eye out for broad use-cases as well as potential outliers.
  • Enlist the assistance of a domain expert to examine your collected and/or annotated data. Someone outside of your team may see biases that your team has missed.
  • Use multi-pass annotation for any project where data accuracy is likely to be skewed. Sentiment analysis, content regulation, and intent identification are examples of this.
  • Assemble a broad staff of data scientists and data labelers.
  • Combine information from numerous sources wherever feasible to ensure data variety.
  • Create explicit standards for data labeling requirements to ensure consistency among data labelers.
  • Establish a gold standard for data labeling. A gold standard is a collection of data that represents the best-labeled data for your task. It allows you to assess the correctness of your team’s annotations.

For every data project, it is critical to be aware of the potential biases in machine learning. By putting in place the correct processes early on and staying on top of data collecting, labeling, and implementation, you can catch it before it becomes an issue or respond to it when it arises.