AI bias, also known as algorithm bias is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous or incorrect assumptions in the machine learning process or incomplete data. 

AI bias often stems from problems introduced by the individuals who design and/or train the machine learning systems. These individuals create algorithms that reflect unintended cognitive or social biases or prejudices. The individuals can introduce biases because they use incomplete, faulty or prejudicial data sets to train and/or validate the machine learning systems.

This MKAI Inclusive Artificial Intelligence (AI) Forum explores how we can take steps to reduce the biases in AI

Our expert speakers make the subject approachable and comprehensible to help all of us improve our AI-fluency and understanding of the domain.