Algorithmic Bias

About

Algorithmic bias refers to the systematic errors in AI systems that result in unfair outcomes, often favoring one group over another. This bias can originate from skewed or limited training data, prejudices in design, or socio-technical factors influencing AI development. Examples include hiring algorithms that favor certain demographics and facial recognition systems that perform poorly on diverse populations. Addressing algorithmic bias involves using diverse and representative data, conducting bias audits, and ensuring transparency in AI decision-making processes. While eliminating bias entirely is challenging, these steps can significantly reduce its impact. Algorithmic bias can lead to discrimination and amplify existing social inequalities, making it crucial to address in areas like healthcare, finance, and justice.