Bias in Algorithms

Bias in algorithms describe outcomes from computer systems “which are systematically less favorable to individuals within a particular group and where there is no relevant difference between groups that justifies such harms” [4]. Algorithms, “which are a set of step-by-step instructions that computers follow to perform a task,” are commonplace in the 21st century and can cause various biases [4]. There are multiple causes of bias in algorithms that can be classified into the following four groups: data creation bias, data analysis bias, data evaluation bias, and the demographics of data scientists. As a result of these biases, algorithms can create or perpetuate racism, sexism, and classism. To prevent those biases from a technical standpoint, it is important to monitor and analyze algorithms during data creation, analysis, and evaluation. From a general standpoint, it is important to increase diversity and inclusion in companies to mitigate biases from every aspect.