Biases in Machine Learning

Most common reasons why biases get introduced in ML models

Whether you like it or not, the impact of machine learning on your life is growing very rapidly. Machine learning algorithms determine whether you would get the mortgage for your dream home, or if your resume would be shortlisted for your next job. It is also changing our workforce rapidly. Robots are taking over warehouses and factories, and self driving cars are threatening to disrupt the jobs of millions of professional drivers across the world. Even law enforcement agencies are increasingly using machine learning to screen for potential criminal leads and assess risks.

Unfortunately, all these advancements in technology may be perpetuating and exacerbating the biases ailing our society. In one of the early examples of algorithmic bias, 60 women and ethnic minorities were denied entry to St. George’s Hospital Medical School per year from 1982 to 1986, because of a new computer-guidance assessment system that denied entry to women and men with “foreign-sounding names” based on historical trends in admissions. Or more recently, in 2016, TayTweets, a chat bot trained by Microsoft on Twitter data started spouting racist tweets.

All these advancements are raising very valid questions about how machine learning practitioners can ensure fairness in their algorithms. What is fair is an age old question. Thankfully, a lot of research has been going on in this area. In this post, I am going to talk about the most common set of problems you might run when trying to ensure that your machine learning model is bias free.

Underrepresentation

One of the most common causes of bias in machine learning algorithms is that the training data is missing samples for underrepresented groups/categories. This is the reason why Siri frequently has a hard time understanding people with accents. This is also what caused the famous Google photos incident where black people were tagged as gorillas. So it is really important to make sure the training data has representation from all the underrepresented groups. Another way to easily detect this early is to deploy a second algorithm which predicts whether the data in production is close to the training data and intervene early if that is not the case.