One of the elements of AI governance includes:

Bias mitigation is a crucial element of AI governance because it directly addresses the ethical considerations of AI systems. In the development and deployment of AI models, it is vital to ensure that these systems do not perpetuate or introduce bias against certain groups of people. Bias can originate from the data used to train AI models, leading to skewed results that may disadvantage specific demographics.

By actively mitigating bias, organizations can enhance the fairness and accountability of their AI applications. This involves implementing strategies such as conducting thorough data reviews, employing diverse training datasets, and utilizing techniques that detect and reduce bias in AI outputs. Addressing bias not only fosters trust in AI systems among users but also helps organizations comply with legal and ethical standards concerning fairness and discrimination.

In contrast, while performance optimization, infrastructure monitoring, and automated data preparation contribute to the efficiency and effectiveness of AI systems, they do not inherently address the ethical implications that come with AI decision-making. These aspects are important for operational success but are separate from the foundational principle of ensuring AI systems uphold fairness and equity through bias mitigation efforts.