3 E's of AI: First an intro to ethical artificial intelligence - IoT Agenda

One of the most common misperceptions I hear about bias is "If I don't use age, gender or race, or similar factors in my model, it's not biased." Even though the same people holding this opinion know that machine learning can learn relationships between data, they don't understand that there are proxies to biased data types in other features that are captured. These proxies are called confounding variables and, as the term indicates, unintended variables can confuse the model into producing biased results. For example, if a model includes the brand and version of an individual's mobile phones, that data can be related to the ability to afford an expensive cell phone -- a characteristic that can impute income. If income is not a factor desired to use directly in the decision, imputing that information from data, such as the type of phone or the size of the purchases that the individual makes, introduces bias into the model.