Understanding algorithmic bias
Algorithmic bias is a pivotal issue in the world of ML. It occurs when a system, intentionally or not, generates outputs that are unfair or systematically prejudiced toward certain individuals or groups. This prejudice often originates from the fact that these systems learn from existing data, which itself can be riddled with inherent societal bias.
Fairness, as it relates to ML, is defined as the absence of any bias. While it might sound simple, achieving fairness can be an intricate process that calls for careful management at every step of model creation.
To paint a more detailed picture, let’s consider protected features. These are attributes that could potentially introduce bias into the system. They can be legally mandated, such as race and gender, or stem from organizational values, such as location or zip code. While seemingly benign, these features, when used in an ML model, can result in decisions that are biased or discriminatory...
