What is the AI algorithmic bias as described in the chapter, and how does it differ from intentional bias?

AI algorithmic bias is an unintentional failure in which AI analyzes big data but does not weigh factors according to desired human criteria such as equitable balance. It differs from intentional bias because it is not deliberate and may go unnoticed until harmful consequences emerge, whereas intentional bias is a purposeful act by someone using AI for unethical ends.

The chapter describes AI algorithmic bias as a phenomenon that occurs when AI analyzes big data but fails to weight factors according to desired human criteria, particularly the criterion of equitable balance. It is not deliberate; rather, it arises because the datasets are too large and complex for humans to understand directly, so people must rely on the patterns AI reports. This bias parallels human bias studied in psychology, but it may never be detected in AI until it is too late. The key contrast is that algorithmic bias is not on purpose, while intentional bias would involve a deliberate choice to skew or misuse AI for a particular unethical outcome or agenda.