We take algorithms for granted and assume that they are unbiased and neutral. An algorithm by definition, according to Merriam-Webster, is a “set of rules a machine [... specifically a computer] follows to achieve a particular goal.” As these rules are designed by humans, they can contain flaws that can lead to biases. A few outliers are expected, but when it leads to bias against certain groups, it can be problematic. Let's take an example - a few years back, Amazon attempted to create a hiring algorithm to efficiently select candidates. However, due to the nature of the historical data used to train the algorithm, it was biased against female applicants. The goal of this talk is to educate on algorithmic bias, present case studies to highlight the adverse effects of algorithmic bias, and address prevention strategies.
