“Data is a reflection of the inequalities that exist in the world”–Beena Ammanath, AI for Good Keynote. While this might be true, developers have great potential to curb model bias and data bias in their computer vision systems.
Testing whether bias is present in a computer vision system is key to understanding how it will perform in operation. Bias manifests itself in numerous ways, from data collection and annotation to the features that a model uses for prediction.
Let’s start by looking at data representativity and the model tests that empower you to uncover pesky biases early in your development process.
Collecting data.
Bias can first appear when collecting and annotating data. The data that you use to build and evaluate a computer vision model must reflect what you intend to use it for: this is referred to as data representativity.
A radiology diagnostic tool to be deployed in southern France must be evaluated on patients from local demographics. The diagnostic tool should also be evaluated on images captured with machines present in the target hospitals. Past research has focused on guidelines that can be followed when collecting and annotating data for training and testing to mitigate such bias.
How do you know if you have the data that matters?
Once you have collected data, it is essential to confirm that it is representative of the target population. While establishing this from image data alone is challenging, image metadata can prove to be very useful. In previous posts, we have introduced the notion of metadata and why it contains semantic information key to evaluating machine learning models–in particular in computer vision. If the sex and age of patients are available, as well as the model of the machine that was used for the collection of the images, we can create unit tests to check data for each relevant slice is present in the datasets. This way we can build up a comprehensive test suite, that allows us to ensure the data as a whole is representative and identify areas where it isn’t–thus effectively guiding the data collection process.
Leave no outlier behind.
Finally, representativity in the literature refers to a match to the target population: for example, if 99.9% of the target population is between 20 and 70 years old, an evaluation dataset should reflect this. This however disregards the importance of the tails of the distribution and is a key difference between building prototypes and production-ready systems. Indeed, an ML model may achieve excellent accuracy on an evaluation dataset containing data in the 20 to 70-year-old range, even if it performs poorly on 80-year-olds. If the product is intended to work on patients of all ages, then it is paramount to explicitly test on slices belonging to the tail of the distribution, even if they are rarely encountered in practice.
As in the illustration below, aggregate evaluation metrics, such as accuracy, precision, and recall may be misleading: it is important to explicitly measure performance for all relevant slices.
In conclusion, find out who your target groups are, big or small, and that you have enough data for all of them. You can use metadata as a tool to find groups that matter.
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