Developing mechanisms to prevent ML bias from spreading throughout the system

Unfortunately, it is generally not possible to completely remove bias from ML as we often do not have access to the attributes needed to reduce the bias. However, we can reduce the bias and reduce the risk that the bias spreads to the entire system.

Awareness and education are some of the most important measures that we can use to manage bias in software systems. We need to understand the potential sources of bias and their implications. We also need to identify biases related to protected attributes (for example, gender) and identify whether other attributes can be correlated with them (for example, occupation and address). Then, we need to educate our team about the ethical implications of biased models.

Then, we need to diversify our data collection. We must ensure that the data we collect is representative of the population we’re to model. To avoid over-representing or under-representing...