Bias in AI - What is it, why does it happen and can it be fixed?

Bias, understood to be an inclination or prejudice for or against one person or group, especially in a way considered to be unfair, is rife in AI and ML. In fact, algorithmic bias in AI systems can take varied forms such as gender bias, racial prejudice and age discrimination. In regard to bias in AI, it is widely recognized that there are two prominent types. These manifest themselves as cognitive bias, meaning that bias is inserted into algorithms through designers introducing them into models or using training data already inclusive of bias, and also lack of complete data, therefore not being representative of the population, culture and encompassing of all demographics. Kishore Karra, Executive Director, Model Risk Governance & Review at J.P. Morgan recently discussed bias in AI models, suggesting that it is at an individual level which bias can creep in: Kishore also recognized that bias can creep into machine learning models unintentionally and it's important to identify sources of bias.