How to Mitigate Negative Algorithmic Biases in Machine Learning

Machine learning models or algorithms have shown over the past few years that they can exhibit human traits like racism and sexism by misidentifying black people as gorillas (Barr, 2015) or perpetuating gender income inequality through ad suggestions (Datta et al., 2015). Algorithmic bias, however, is not inherently problematic. Given the potential harm machine learning can cause, how can South African organisations mitigate against problematic algorithmic bias in their data and models? This essay will use the taxonomy of algorithmic bias created by Danks and London (2017) to differentiate between the various types of algorithmic bias and give examples of how problematic bias might perpetuate immoral discrimination within a South African context. Specifically, it will examine training data bias, algorithmic focus bias and transfer context bias. The most intuitive bias is training data bias; if biased data are used, the resulting model reflects that bias.