Algorithmic bias

A subtle yet insidious force, algorithmic bias is the unintended and often harmful prejudice that arises from the design or implementation of algorithms, perpetuating existing inequalities and undermining the fairness and impartiality of data-driven decision-making.

Example

A hiring platform that uses an algorithm to rank job applicants based on various factors might inadvertently introduce bias if the training data used to develop the algorithm contains historical patterns of discrimination. For instance, if the algorithm is trained on a dataset where male applicants were historically favored over female applicants, it may continue to rank male applicants higher, perpetuating gender bias in the hiring process. Addressing algorithmic bias is crucial to ensuring equitable and fair outcomes in automated decision-making systems.