Algorithmic Bias

Algorithmic bias or AI bias refers to the unfair or discriminatory outcomes of AI, resulting from the use of algorithms, to a certain group of people. It is a series of systematic and repeatable errors occurs in a computer system that favors one group of people in ways which doesn't match with the intent of the algorithm functions.

Algorithmic bias is a growing concern for all with artificial intelligence (AI) and machine learning (ML) spreading across industries. It is necessary to understand the biases that algorithms can develop over time since they are not immune to flaws in human design. Design flaws, biased data, and conscious or subconscious human prejudice throughout the development stage are some of the reasons for the occurrence of algorithmic biases.

AI & machine learning operationalization (MLOps) software can be used to monitor and mitigate the potential risks of bias proactively. The consequences that disrupt the well-being of different societal groups can be prevented by this software.

Data bias, Sampling bias, Interaction bias, Group attribution bias, and Feedback loop bias are five common types of biases that can exist in an algorithm.

Designing with inclusion, testing before and after deployment, using synthetic data and focusing on AI explainability are few best practices to keep in mind to prevent algorithmic bias.