Human bias is a huge problem for AI. Here's how we're going to fix it

Machines don't actually have bias. AI doesn't'want' something to be true or false for reasons that can't be explained through logic. Unfortunately human bias exists in machine learning from the creation of an algorithm to the interpretation of data – and until now hardly anyone has tried to solve this huge problem. A team of scientists from Czech Republic and Germany recently conducted research to determine the effect human cognitive bias has on interpreting the output used to create machine learning rules. The team's white paper explains how 20 different cognitive biases could potentially alter the development of machine learning rules and proposes methods for "debiasing" them. Biases such as "confirmation bias" (when a person accepts a result because it confirms a previous belief) or "availability bias" (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless.